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class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/96156199/The_%C3%93MICAS_alliance_an_international_research_program_on_multi_omics_for_crop_breeding_optimization"><img alt="Research paper thumbnail of The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/98133121/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/96156199/The_%C3%93MICAS_alliance_an_international_research_program_on_multi_omics_for_crop_breeding_optimization">The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization</a></div><div class="wp-workCard_item"><span>Frontiers in Plant Science</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The OMICAS alliance is part of the Colombian government’s Scientific Ecosystem, established betwe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The OMICAS alliance is part of the Colombian government’s Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program’s kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools t...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="68b485a80cef0c2f9206c9077dec2ba8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":98133121,"asset_id":96156199,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/98133121/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="96156199"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="96156199"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 96156199; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=96156199]").text(description); $(".js-view-count[data-work-id=96156199]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 96156199; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='96156199']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 96156199, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "68b485a80cef0c2f9206c9077dec2ba8" } } $('.js-work-strip[data-work-id=96156199]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":96156199,"title":"The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization","translated_title":"","metadata":{"abstract":"The OMICAS alliance is part of the Colombian government’s Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83495729"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83495729/Spectral_Evolution_of_Twitter_Mention_Networks"><img alt="Research paper thumbnail of Spectral Evolution of Twitter Mention Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83495729/Spectral_Evolution_of_Twitter_Mention_Networks">Spectral Evolution of Twitter Mention Networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This papers applies the spectral evolution model presented in [5] to networks of mentions between...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This papers applies the spectral evolution model presented in [5] to networks of mentions between Twitter users who identified messages with the most popular political hashtags in Colombia (during the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). The model characterizes the dynamics of each mention network (i.e., how new edges are established) in terms of the eigen decomposition of its adjacency matrix. It assumes that as new edges are established the eigenvalues change, while the eigenvectors remain constant. The goal of our work is to evaluate various link prediction methods that underlie the spectral evolution model. In particular, we consider prediction methods based on graph kernels and a learning algorithm that tries to estimate the trajectories of the spectrum. Our results show that the learning algorithm tends to outperform the kernel methods at predicting the formation of new edges.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83495729"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83495729"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83495729; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83495729]").text(description); $(".js-view-count[data-work-id=83495729]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83495729; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83495729']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83495729, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=83495729]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83495729,"title":"Spectral Evolution of Twitter Mention Networks","translated_title":"","metadata":{"abstract":"This papers applies the spectral evolution model presented in [5] to networks of mentions between Twitter users who identified messages with the most popular political hashtags in Colombia (during the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). 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Our results show that the learning algorithm tends to outperform the kernel methods at predicting the formation of new edges.","publisher":"COMPLEX NETWORKS","publication_date":{"day":null,"month":null,"year":2019,"errors":{}}},"translated_abstract":"This papers applies the spectral evolution model presented in [5] to networks of mentions between Twitter users who identified messages with the most popular political hashtags in Colombia (during the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). The model characterizes the dynamics of each mention network (i.e., how new edges are established) in terms of the eigen decomposition of its adjacency matrix. It assumes that as new edges are established the eigenvalues change, while the eigenvectors remain constant. The goal of our work is to evaluate various link prediction methods that underlie the spectral evolution model. In particular, we consider prediction methods based on graph kernels and a learning algorithm that tries to estimate the trajectories of the spectrum. Our results show that the learning algorithm tends to outperform the kernel methods at predicting the formation of new edges.","internal_url":"https://www.academia.edu/83495729/Spectral_Evolution_of_Twitter_Mention_Networks","translated_internal_url":"","created_at":"2022-07-21T02:37:29.120-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":316649,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Spectral_Evolution_of_Twitter_Mention_Networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":316649,"first_name":"Jorge","middle_initials":null,"last_name":"Finke","page_name":"Finke","domain_name":"javeriana","created_at":"2011-01-26T04:19:09.875-08:00","display_name":"Jorge Finke","url":"https://javeriana.academia.edu/Finke"},"attachments":[],"research_interests":[],"urls":[{"id":22332383,"url":"https://doi.org/10.1007/978-3-030-36687-2_44"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83495716"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83495716/Detecting_Hotspots_on_Networks"><img alt="Research paper thumbnail of Detecting Hotspots on Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83495716/Detecting_Hotspots_on_Networks">Detecting Hotspots on Networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Traditional approaches for measuring the concentration of events pay little attention to the effe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Traditional approaches for measuring the concentration of events pay little attention to the effects of topological properties. To overcome this limitation, our work develops a theoretical framework to determine whether events are concentrated on a subset of interconnected nodes. We focus on low-clustered networks with regular, Poisson, and power-law degree distributions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83495716"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83495716"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83495716; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83495716]").text(description); $(".js-view-count[data-work-id=83495716]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83495716; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83495716']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83495716, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=83495716]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83495716,"title":"Detecting Hotspots on Networks","translated_title":"","metadata":{"abstract":"Traditional approaches for measuring the concentration of events pay little attention to the effects of topological properties. 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We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. Simulation results illustrate how the number of new edges and the process of reciprocity significantly impact t...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="01d945f1df798a27fc2cd1ef00b98ebc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88818823,"asset_id":83495664,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88818823/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83495664"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83495664"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83495664; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83495664]").text(description); $(".js-view-count[data-work-id=83495664]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83495664; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83495664']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83495664, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "01d945f1df798a27fc2cd1ef00b98ebc" } } $('.js-work-strip[data-work-id=83495664]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83495664,"title":"Characterizing the head of the degree distributions of growing networks","translated_title":"","metadata":{"abstract":"The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. Simulation results illustrate how the number of new edges and the process of reciprocity significantly impact t...","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"arXiv: Physics and Society"},"translated_abstract":"The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. Simulation results illustrate how the number of new edges and the process of reciprocity significantly impact t...","internal_url":"https://www.academia.edu/83495664/Characterizing_the_head_of_the_degree_distributions_of_growing_networks","translated_internal_url":"","created_at":"2022-07-21T02:35:33.392-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":316649,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":88818823,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88818823/thumbnails/1.jpg","file_name":"2011.04643v2.pdf","download_url":"https://www.academia.edu/attachments/88818823/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Characterizing_the_head_of_the_degree_di.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88818823/2011.04643v2-libre.pdf?1658404639=\u0026response-content-disposition=attachment%3B+filename%3DCharacterizing_the_head_of_the_degree_di.pdf\u0026Expires=1733264972\u0026Signature=crfMELZt6T93nRAdXSb3iBlZ2zi1EHaQ5CmpulLamjV6t-wBtB7JAwezqmbhX6amM302nCyq8RecuGimR5QK4TUriYuvrcy5mvDvpojpKvihS-KclHehdqxh~oXLagX9pJkwm8GKDicY5J2fGkI5ZPGAyeSMxKY2teEGZ5zlyecGQqQqoctuIebGJxnYOlZihhMtZd8tiqA6F5OTIcaFQDyW3mNBsM9lNyhlifI-yv-XHjHJnn2lxNeEbJ~20NhgQhuZjo8TMDQg1J7PBq0pyQZkhHRzj3rIFLpE9WzDoVCa8nSN4KPT20fn4zMOB~PePCvQvnODBmBqM0lvDJaqIw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Characterizing_the_head_of_the_degree_distributions_of_growing_networks","translated_slug":"","page_count":18,"language":"en","content_type":"Work","owner":{"id":316649,"first_name":"Jorge","middle_initials":null,"last_name":"Finke","page_name":"Finke","domain_name":"javeriana","created_at":"2011-01-26T04:19:09.875-08:00","display_name":"Jorge Finke","url":"https://javeriana.academia.edu/Finke"},"attachments":[{"id":88818823,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88818823/thumbnails/1.jpg","file_name":"2011.04643v2.pdf","download_url":"https://www.academia.edu/attachments/88818823/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Characterizing_the_head_of_the_degree_di.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88818823/2011.04643v2-libre.pdf?1658404639=\u0026response-content-disposition=attachment%3B+filename%3DCharacterizing_the_head_of_the_degree_di.pdf\u0026Expires=1733264972\u0026Signature=crfMELZt6T93nRAdXSb3iBlZ2zi1EHaQ5CmpulLamjV6t-wBtB7JAwezqmbhX6amM302nCyq8RecuGimR5QK4TUriYuvrcy5mvDvpojpKvihS-KclHehdqxh~oXLagX9pJkwm8GKDicY5J2fGkI5ZPGAyeSMxKY2teEGZ5zlyecGQqQqoctuIebGJxnYOlZihhMtZd8tiqA6F5OTIcaFQDyW3mNBsM9lNyhlifI-yv-XHjHJnn2lxNeEbJ~20NhgQhuZjo8TMDQg1J7PBq0pyQZkhHRzj3rIFLpE9WzDoVCa8nSN4KPT20fn4zMOB~PePCvQvnODBmBqM0lvDJaqIw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics"}],"urls":[{"id":22332355,"url":"https://arxiv.org/pdf/2011.04643v2.pdf"}]}, dispatcherData: dispatcherData }); 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This includes a representation for (i) the tendency of regular nodes to connect with similar others (i.e., establish homophilic relationships); and (ii) the tendency of anomalous nodes to connect to random targets (i.e., establish random connections across the network). Our approach is motivated by the desire to design scalable strategies for detecting signatures of anomalous behavior, using a formal representation to take into account the evolution of network properties. In particular, we assume that regular nodes are distributed across two communities (of different size), and propose an algorithm that identifies anomalous nodes based on both geometric and spectral measures. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317453"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317453/A_top_down_supervised_learning_approach_to_hierarchical_multi_label_classification_in_networks"><img alt="Research paper thumbnail of A top-down supervised learning approach to hierarchical multi-label classification in networks" class="work-thumbnail" src="https://attachments.academia-assets.com/86072977/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317453/A_top_down_supervised_learning_approach_to_hierarchical_multi_label_classification_in_networks">A top-down supervised learning approach to hierarchical multi-label classification in networks</a></div><div class="wp-workCard_item"><span>Applied Network Science</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Node classification is the task of inferring or predicting missing node attributes from informati...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi-label classification, where the attributes to be inferred can be specified as a strict poset. It is based on a top-down classification approach that addresses hierarchical multi-label classification with supervised learning by building a local classifier per class. The proposed model is showcased with a case study on the prediction of gene functions for Oryza sativa Japonica , a variety of rice. It is compared to the Hierarchical Binomial-Neighborhood, a probabilistic model, by evaluating both approaches in terms of prediction performance and computational cost. 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Sci.","ai_title_tag":"Top-Down Supervised Learning for Hierarchical Multi-Label Classification","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"Applied Network Science"},"translated_abstract":"Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi-label classification, where the attributes to be inferred can be specified as a strict poset. It is based on a top-down classification approach that addresses hierarchical multi-label classification with supervised learning by building a local classifier per class. The proposed model is showcased with a case study on the prediction of gene functions for Oryza sativa Japonica , a variety of rice. It is compared to the Hierarchical Binomial-Neighborhood, a probabilistic model, by evaluating both approaches in terms of prediction performance and computational cost. The results in this work support the working hypothesis that the proposed model can achieve good levels of prediction efficiency, while scaling up in relation to the state of the art.","internal_url":"https://www.academia.edu/79317453/A_top_down_supervised_learning_approach_to_hierarchical_multi_label_classification_in_networks","translated_internal_url":"","created_at":"2022-05-17T09:06:21.716-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":316649,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":86072977,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86072977/thumbnails/1.jpg","file_name":"s41109-022-00445-3.pdf","download_url":"https://www.academia.edu/attachments/86072977/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_top_down_supervised_learning_approach.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86072977/s41109-022-00445-3-libre.pdf?1652804002=\u0026response-content-disposition=attachment%3B+filename%3DA_top_down_supervised_learning_approach.pdf\u0026Expires=1733264972\u0026Signature=GDbndfzqiL7I4DaoZrDQ9GvmBK-jcL86oRtmsnMaQkIZjVpIn~pjZ2a-afphGEFatyUU55VJ6p~DYQAbJzQIk3dmzNyp93b6S~Foq3Cqyl-xsACQdEZhgGrMVCCqmU5OS5d8xJJyYf8UbRQNW6jDe9pOpB~To1kcwumhq60A7XQGmG6RH1b26WEWuOWWmJWm192YvE9PJT3nLDZfQidFlukkikA2E4fQEg5qODYAFNARRneCDUMPhthsU1idfVs2PPji0p~qjDe5nRCI0VbBk0b3n5dIWoO6ATgefx7tbAzZMIHhcyJe5RJ9tWb-0PKVsN1DzjKjlZRL-3AG~6Z~Fg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_top_down_supervised_learning_approach_to_hierarchical_multi_label_classification_in_networks","translated_slug":"","page_count":17,"language":"en","content_type":"Work","owner":{"id":316649,"first_name":"Jorge","middle_initials":null,"last_name":"Finke","page_name":"Finke","domain_name":"javeriana","created_at":"2011-01-26T04:19:09.875-08:00","display_name":"Jorge Finke","url":"https://javeriana.academia.edu/Finke"},"attachments":[{"id":86072977,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86072977/thumbnails/1.jpg","file_name":"s41109-022-00445-3.pdf","download_url":"https://www.academia.edu/attachments/86072977/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_top_down_supervised_learning_approach.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86072977/s41109-022-00445-3-libre.pdf?1652804002=\u0026response-content-disposition=attachment%3B+filename%3DA_top_down_supervised_learning_approach.pdf\u0026Expires=1733264972\u0026Signature=GDbndfzqiL7I4DaoZrDQ9GvmBK-jcL86oRtmsnMaQkIZjVpIn~pjZ2a-afphGEFatyUU55VJ6p~DYQAbJzQIk3dmzNyp93b6S~Foq3Cqyl-xsACQdEZhgGrMVCCqmU5OS5d8xJJyYf8UbRQNW6jDe9pOpB~To1kcwumhq60A7XQGmG6RH1b26WEWuOWWmJWm192YvE9PJT3nLDZfQidFlukkikA2E4fQEg5qODYAFNARRneCDUMPhthsU1idfVs2PPji0p~qjDe5nRCI0VbBk0b3n5dIWoO6ATgefx7tbAzZMIHhcyJe5RJ9tWb-0PKVsN1DzjKjlZRL-3AG~6Z~Fg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"}],"urls":[{"id":20535003,"url":"https://doi.org/10.1007/s41109-022-00445-3"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317452"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317452/Characterizing_the_head_of_the_degrees_distributions_of_growing_networks"><img alt="Research paper thumbnail of Characterizing the head of the degrees distributions of growing networks" class="work-thumbnail" src="https://attachments.academia-assets.com/86072979/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317452/Characterizing_the_head_of_the_degrees_distributions_of_growing_networks">Characterizing the head of the degrees distributions of growing networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The analysis in this paper helps to explain the formation of growing networks with degree distrib...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. Simulation results illustrate how the number of new edges and the process of reciprocity significantly impact t...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a01ea339803b63534df3dc6106a6c2b0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072979,"asset_id":79317452,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072979/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317452"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317452"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317452; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317452]").text(description); $(".js-view-count[data-work-id=79317452]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317452; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317452']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317452, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a01ea339803b63534df3dc6106a6c2b0" } } $('.js-work-strip[data-work-id=79317452]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317452,"title":"Characterizing the head of the degrees distributions of growing networks","translated_title":"","metadata":{"abstract":"The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. Simulation results illustrate how the number of new edges and the process of reciprocity significantly impact t...","publication_date":{"day":9,"month":11,"year":2020,"errors":{}}},"translated_abstract":"The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. Simulation results illustrate how the number of new edges and the process of reciprocity significantly impact t...","internal_url":"https://www.academia.edu/79317452/Characterizing_the_head_of_the_degrees_distributions_of_growing_networks","translated_internal_url":"","created_at":"2022-05-17T09:06:21.597-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":316649,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":86072979,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86072979/thumbnails/1.jpg","file_name":"2011.04643v1.pdf","download_url":"https://www.academia.edu/attachments/86072979/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Characterizing_the_head_of_the_degrees_d.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86072979/2011.04643v1-libre.pdf?1652804997=\u0026response-content-disposition=attachment%3B+filename%3DCharacterizing_the_head_of_the_degrees_d.pdf\u0026Expires=1733264972\u0026Signature=Ut0AaNvq6j2-1VYQSftvmfpkBJlCThrJ5leGmeRzk~Km02DF35dtM-hqDe-h81Z5rsE0WxYSpexEcT9hpBtsSdTE0YssRhYVPgNTzZyqxR7pf4ysOKO7L7hXyOsB2l1ejCoPpOeRVn~VLR2mh23UFIHz1TrMkeE9ad27Tov66xZTI9ATvMOfSkAVR-WtXNKDwZbvxily201COwM7kdD4MGVPiutmeGOu6GtKLJr1jStGMn-hIdES~irndaep5zo0aNSApCE5Vkz82FcvQ51W1eEIacYb66MlQytIiZa2bDFA5ExdHbw5CrqRAaeTT6Rlfm56C59-rcuegBXufvad~Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Characterizing_the_head_of_the_degrees_distributions_of_growing_networks","translated_slug":"","page_count":18,"language":"en","content_type":"Work","owner":{"id":316649,"first_name":"Jorge","middle_initials":null,"last_name":"Finke","page_name":"Finke","domain_name":"javeriana","created_at":"2011-01-26T04:19:09.875-08:00","display_name":"Jorge Finke","url":"https://javeriana.academia.edu/Finke"},"attachments":[{"id":86072979,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86072979/thumbnails/1.jpg","file_name":"2011.04643v1.pdf","download_url":"https://www.academia.edu/attachments/86072979/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Characterizing_the_head_of_the_degrees_d.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86072979/2011.04643v1-libre.pdf?1652804997=\u0026response-content-disposition=attachment%3B+filename%3DCharacterizing_the_head_of_the_degrees_d.pdf\u0026Expires=1733264972\u0026Signature=Ut0AaNvq6j2-1VYQSftvmfpkBJlCThrJ5leGmeRzk~Km02DF35dtM-hqDe-h81Z5rsE0WxYSpexEcT9hpBtsSdTE0YssRhYVPgNTzZyqxR7pf4ysOKO7L7hXyOsB2l1ejCoPpOeRVn~VLR2mh23UFIHz1TrMkeE9ad27Tov66xZTI9ATvMOfSkAVR-WtXNKDwZbvxily201COwM7kdD4MGVPiutmeGOu6GtKLJr1jStGMn-hIdES~irndaep5zo0aNSApCE5Vkz82FcvQ51W1eEIacYb66MlQytIiZa2bDFA5ExdHbw5CrqRAaeTT6Rlfm56C59-rcuegBXufvad~Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":20535002,"url":"https://arxiv.org/pdf/2011.04643v1.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317451"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317451/In_silico_Gene_Annotation_Prediction_Using_the_Co_expression_Network_Structure"><img alt="Research paper thumbnail of In-silico Gene Annotation Prediction Using the Co-expression Network Structure" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317451/In_silico_Gene_Annotation_Prediction_Using_the_Co_expression_Network_Structure">In-silico Gene Annotation Prediction Using the Co-expression Network Structure</a></div><div class="wp-workCard_item"><span>Complex Networks and Their Applications VIII</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Identifying which genes are involved in particular biological processes is relevant to understand...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Identifying which genes are involved in particular biological processes is relevant to understand the structure and function of a genome. A number of techniques have been proposed that aim to annotate genes, i.e., identify unknown biological associations between biological processes and genes. The ultimate goal of these techniques is to narrow down the search for promising candidates to carry out further studies through in-vivo experiments. This paper presents an approach for the in-silico prediction of functional gene annotations. It uses existing knowledge body of gene annotations of a given genome and the topological properties of its gene co-expression network, to train a supervised machine learning model that is designed to discover unknown annotations. The approach is applied to Oryza Sativa Japonica (a variety of rice). Our results show that the topological properties help in obtaining a more precise prediction for annotating genes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317451"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317451"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317451; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317451]").text(description); $(".js-view-count[data-work-id=79317451]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317451; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317451']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317451, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79317451]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317451,"title":"In-silico Gene Annotation Prediction Using the Co-expression Network Structure","translated_title":"","metadata":{"abstract":"Identifying which genes are involved in particular biological processes is relevant to understand the structure and function of a genome. A number of techniques have been proposed that aim to annotate genes, i.e., identify unknown biological associations between biological processes and genes. The ultimate goal of these techniques is to narrow down the search for promising candidates to carry out further studies through in-vivo experiments. This paper presents an approach for the in-silico prediction of functional gene annotations. It uses existing knowledge body of gene annotations of a given genome and the topological properties of its gene co-expression network, to train a supervised machine learning model that is designed to discover unknown annotations. The approach is applied to Oryza Sativa Japonica (a variety of rice). Our results show that the topological properties help in obtaining a more precise prediction for annotating genes.","publisher":"Springer International Publishing","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Complex Networks and Their Applications VIII"},"translated_abstract":"Identifying which genes are involved in particular biological processes is relevant to understand the structure and function of a genome. A number of techniques have been proposed that aim to annotate genes, i.e., identify unknown biological associations between biological processes and genes. The ultimate goal of these techniques is to narrow down the search for promising candidates to carry out further studies through in-vivo experiments. This paper presents an approach for the in-silico prediction of functional gene annotations. It uses existing knowledge body of gene annotations of a given genome and the topological properties of its gene co-expression network, to train a supervised machine learning model that is designed to discover unknown annotations. The approach is applied to Oryza Sativa Japonica (a variety of rice). Our results show that the topological properties help in obtaining a more precise prediction for annotating genes.","internal_url":"https://www.academia.edu/79317451/In_silico_Gene_Annotation_Prediction_Using_the_Co_expression_Network_Structure","translated_internal_url":"","created_at":"2022-05-17T09:06:21.465-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":316649,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"In_silico_Gene_Annotation_Prediction_Using_the_Co_expression_Network_Structure","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":316649,"first_name":"Jorge","middle_initials":null,"last_name":"Finke","page_name":"Finke","domain_name":"javeriana","created_at":"2011-01-26T04:19:09.875-08:00","display_name":"Jorge Finke","url":"https://javeriana.academia.edu/Finke"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":4233,"name":"Computational Biology","url":"https://www.academia.edu/Documents/in/Computational_Biology"},{"id":38072,"name":"Annotation","url":"https://www.academia.edu/Documents/in/Annotation"},{"id":865697,"name":"In Silico","url":"https://www.academia.edu/Documents/in/In_Silico"}],"urls":[{"id":20535001,"url":"http://link.springer.com/content/pdf/10.1007/978-3-030-36683-4_64"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317450"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317450/Dynamics_of_group_cohesion_in_homophilic_networks"><img alt="Research paper thumbnail of Dynamics of group cohesion in homophilic networks" class="work-thumbnail" src="https://attachments.academia-assets.com/86072973/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317450/Dynamics_of_group_cohesion_in_homophilic_networks">Dynamics of group cohesion in homophilic networks</a></div><div class="wp-workCard_item"><span>2017 IEEE 56th Annual Conference on Decision and Control (CDC)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Understanding cohesion and homophily in empirical networks allows us build better personalization...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Understanding cohesion and homophily in empirical networks allows us build better personalization and recommendation systems. This paper proposes a network model that explains the emergence of cohesion and homophily as an aggregate outcome at the group- and network-level. We introduce two simple mechanisms that capture the underlying tendencies of nodes to connect with similar and different others. 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They are a fram...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Social network models aim to capture the complex structure of social connections. They are a framework for the design of control algorithms that take into account relationships, interactions, and communications between social actors. Based on three formation mechanisms — random attachment, triad formation, and network response — our work characterizes the dynamics of the degree distributions of social networks. In particular, we show that the complementary cumulative in- and out-degree distributions of highly clustered, reciprocal networks can be approximated by infinite dimensional time-varying linear systems. Furthermore, we determine the invariance of both limit distributions and the stability properties of the average degree.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317449"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317449"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317449; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317449]").text(description); $(".js-view-count[data-work-id=79317449]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317449; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317449']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317449, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79317449]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317449,"title":"Dynamics of degree distributions of social networks","translated_title":"","metadata":{"abstract":"Social network models aim to capture the complex structure of social connections. 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The model is parametric on two probability distribution functions governing block production and communication delay, which are key to capture the complexity of the mechanism used to synchronize the many distributed local copies of a blockchain. The proposed model is equipped with simulation algorithms for both bounded and unbounded number of distributed copies of the blockchain. They are used to study fast blockchain systems, i.e., blockchains in which the average time of block production can match the average time of message broadcasting used for blockchain synchronization. In particular, the model and the algorithms are useful to understand efficiency criteria associated with fast blockchains for identifying, e.g., when increasing the block production will have negative impact on the stability of the distributed...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="96fa3588541396de21a710275ed3202a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072914,"asset_id":79317448,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072914/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317448"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317448"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317448; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317448]").text(description); $(".js-view-count[data-work-id=79317448]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317448; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317448']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317448, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "96fa3588541396de21a710275ed3202a" } } $('.js-work-strip[data-work-id=79317448]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317448,"title":"A Random Network Model for the Analysis of Blockchain Designs with Communication Delay","translated_title":"","metadata":{"abstract":"This paper proposes a random network model for blockchains, a distributed hierarchical data structure of blocks that has found several applications in various industries. 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In particular, the model and the algorithms are useful to understand efficiency criteria associated with fast blockchains for identifying, e.g., when increasing the block production will have negative impact on the stability of the distributed...","publisher":"ArXiv","ai_title_tag":"Random Network Model for Blockchain with Communication Delay","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"ArXiv"},"translated_abstract":"This paper proposes a random network model for blockchains, a distributed hierarchical data structure of blocks that has found several applications in various industries. The model is parametric on two probability distribution functions governing block production and communication delay, which are key to capture the complexity of the mechanism used to synchronize the many distributed local copies of a blockchain. The proposed model is equipped with simulation algorithms for both bounded and unbounded number of distributed copies of the blockchain. They are used to study fast blockchain systems, i.e., blockchains in which the average time of block production can match the average time of message broadcasting used for blockchain synchronization. In particular, the model and the algorithms are useful to understand efficiency criteria associated with fast blockchains for identifying, e.g., when increasing the block production will have negative impact on the stability of the distributed...","internal_url":"https://www.academia.edu/79317448/A_Random_Network_Model_for_the_Analysis_of_Blockchain_Designs_with_Communication_Delay","translated_internal_url":"","created_at":"2022-05-17T09:06:21.025-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":316649,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":86072914,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86072914/thumbnails/1.jpg","file_name":"1909.06435v1.pdf","download_url":"https://www.academia.edu/attachments/86072914/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Random_Network_Model_for_the_Analysis.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86072914/1909.06435v1-libre.pdf?1652804039=\u0026response-content-disposition=attachment%3B+filename%3DA_Random_Network_Model_for_the_Analysis.pdf\u0026Expires=1733264972\u0026Signature=JZjin0tvmx9BPQQ~mKRHrw-xOtA7oSky6ACCU2Z69tN3oaThqgfrgp1Q1dWmkJKX3DpnLwyXzoKnsJtYU0znMhdKOU7cafxM~MKDlQf19cuXULGw-O31VPz05hjsLyWIVpmVUDAA5EiiJqoFof8h7daBlLEjrkBBr3l1PJopBEEQYhJt3s4vCLLk5dhmzsN-ORGkhc58bEvdJRl5vvpJjGze6fw8MSBXDrI0BP-bLfp9F4G6VfDD9AJ550mZyUTDCFWOzFSB~dmkwcIaXOKm5EYxLlqbx3f1pzVJZqDmtgtn6kVyUMaN4onRla2zTw03fIY9hG2mnYjOn4NWqXRKhw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Random_Network_Model_for_the_Analysis_of_Blockchain_Designs_with_Communication_Delay","translated_slug":"","page_count":18,"language":"en","content_type":"Work","owner":{"id":316649,"first_name":"Jorge","middle_initials":null,"last_name":"Finke","page_name":"Finke","domain_name":"javeriana","created_at":"2011-01-26T04:19:09.875-08:00","display_name":"Jorge Finke","url":"https://javeriana.academia.edu/Finke"},"attachments":[{"id":86072914,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86072914/thumbnails/1.jpg","file_name":"1909.06435v1.pdf","download_url":"https://www.academia.edu/attachments/86072914/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Random_Network_Model_for_the_Analysis.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86072914/1909.06435v1-libre.pdf?1652804039=\u0026response-content-disposition=attachment%3B+filename%3DA_Random_Network_Model_for_the_Analysis.pdf\u0026Expires=1733264972\u0026Signature=JZjin0tvmx9BPQQ~mKRHrw-xOtA7oSky6ACCU2Z69tN3oaThqgfrgp1Q1dWmkJKX3DpnLwyXzoKnsJtYU0znMhdKOU7cafxM~MKDlQf19cuXULGw-O31VPz05hjsLyWIVpmVUDAA5EiiJqoFof8h7daBlLEjrkBBr3l1PJopBEEQYhJt3s4vCLLk5dhmzsN-ORGkhc58bEvdJRl5vvpJjGze6fw8MSBXDrI0BP-bLfp9F4G6VfDD9AJ550mZyUTDCFWOzFSB~dmkwcIaXOKm5EYxLlqbx3f1pzVJZqDmtgtn6kVyUMaN4onRla2zTw03fIY9hG2mnYjOn4NWqXRKhw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3193313,"name":"arXiv","url":"https://www.academia.edu/Documents/in/arXiv"}],"urls":[{"id":20534998,"url":"https://arxiv.org/pdf/1909.06435v1.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317447"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317447/J_un_2_01_2_Power_law_weighted_networks_from_local_attachments"><img alt="Research paper thumbnail of J un 2 01 2 Power-law weighted networks from local attachments" class="work-thumbnail" src="https://attachments.academia-assets.com/86072913/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317447/J_un_2_01_2_Power_law_weighted_networks_from_local_attachments">J un 2 01 2 Power-law weighted networks from local attachments</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This letter introduces a mechanism for constructing, through a process of distributed decision-ma...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This letter introduces a mechanism for constructing, through a process of distributed decision-making, substrates for the study of collective dynamics on extended power-law weighted networks with both a desired scaling exponent and a fixed clustering coefficient. The analytical results show that the connectivity distribution converges to the scaling behavior often found in social and engineering systems. To illustrate the approach of the proposed framework we generate network substrates that resemble steady state properties of the empirical citation distributions of (i) publications indexed by the Institute for Scientific Information from 1981 to 1997; (ii) patents granted by the U.S. Patent and Trademark Office from 1975 to 1999; and (iii) opinions written by the Supreme Court and the cases they cite from 1754 to 2002.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="737a075284ce1dbdc144dc6de6da98c3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072913,"asset_id":79317447,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072913/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317447"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317447"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317447; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317447]").text(description); $(".js-view-count[data-work-id=79317447]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317447; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317447']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317447, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "737a075284ce1dbdc144dc6de6da98c3" } } $('.js-work-strip[data-work-id=79317447]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317447,"title":"J un 2 01 2 Power-law weighted networks from local attachments","translated_title":"","metadata":{"abstract":"This letter introduces a mechanism for constructing, through a process of distributed decision-making, substrates for the study of collective dynamics on extended power-law weighted networks with both a desired scaling exponent and a fixed clustering coefficient. 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To illustrate the approach of the proposed framework we generate network substrates that resemble steady state properties of the empirical citation distributions of (i) publications indexed by the Institute for Scientific Information from 1981 to 1997; (ii) patents granted by the U.S. Patent and Trademark Office from 1975 to 1999; and (iii) opinions written by the Supreme Court and the cases they cite from 1754 to 2002.","internal_url":"https://www.academia.edu/79317447/J_un_2_01_2_Power_law_weighted_networks_from_local_attachments","translated_internal_url":"","created_at":"2022-05-17T09:06:20.900-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":316649,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":86072913,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86072913/thumbnails/1.jpg","file_name":"Preprint-EPL12.pdf","download_url":"https://www.academia.edu/attachments/86072913/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"J_un_2_01_2_Power_law_weighted_networks.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86072913/Preprint-EPL12-libre.pdf?1652804035=\u0026response-content-disposition=attachment%3B+filename%3DJ_un_2_01_2_Power_law_weighted_networks.pdf\u0026Expires=1733264972\u0026Signature=YBZa-Rm4TxMUTBA3~mdWeWxEcdR8p3GcS-MUOyEdyZsTeEQMks6uvKjlcFEtdcwPh2xHlX8P34hDjyY73XC0shBhTJw1Xz0XIeTwZpHr3KsTzPMLGEyOF~6XJzzta3Yr1yvr2SMFDWov-sslHQmDk7tHYUhg0bF8NG5-d4RILbX2Indd3wJ7NTAFpAErz5Wj3YTjWwNU2US9y6mDOA~Qlg2Fb9MCbpnYjtb8QkeHxF1CMeLYYNlyQDXxjsHgn268fIWjk0vURK1ryhWsJNoTDiuoGv4niY7oxSNbULt6iso3NF9UCFvPEnT2zOYcxAdy4ddQmLdmi~nd41ETQUKvMA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"J_un_2_01_2_Power_law_weighted_networks_from_local_attachments","translated_slug":"","page_count":18,"language":"en","content_type":"Work","owner":{"id":316649,"first_name":"Jorge","middle_initials":null,"last_name":"Finke","page_name":"Finke","domain_name":"javeriana","created_at":"2011-01-26T04:19:09.875-08:00","display_name":"Jorge Finke","url":"https://javeriana.academia.edu/Finke"},"attachments":[{"id":86072913,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86072913/thumbnails/1.jpg","file_name":"Preprint-EPL12.pdf","download_url":"https://www.academia.edu/attachments/86072913/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"J_un_2_01_2_Power_law_weighted_networks.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86072913/Preprint-EPL12-libre.pdf?1652804035=\u0026response-content-disposition=attachment%3B+filename%3DJ_un_2_01_2_Power_law_weighted_networks.pdf\u0026Expires=1733264972\u0026Signature=YBZa-Rm4TxMUTBA3~mdWeWxEcdR8p3GcS-MUOyEdyZsTeEQMks6uvKjlcFEtdcwPh2xHlX8P34hDjyY73XC0shBhTJw1Xz0XIeTwZpHr3KsTzPMLGEyOF~6XJzzta3Yr1yvr2SMFDWov-sslHQmDk7tHYUhg0bF8NG5-d4RILbX2Indd3wJ7NTAFpAErz5Wj3YTjWwNU2US9y6mDOA~Qlg2Fb9MCbpnYjtb8QkeHxF1CMeLYYNlyQDXxjsHgn268fIWjk0vURK1ryhWsJNoTDiuoGv4niY7oxSNbULt6iso3NF9UCFvPEnT2zOYcxAdy4ddQmLdmi~nd41ETQUKvMA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":20534997,"url":"http://pmoriano.com/docs/Preprint-EPL12.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317446"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317446/Preferential_attachment_with_power_law_growth_in_the_number_of_new_edges"><img alt="Research paper thumbnail of Preferential attachment with power law growth in the number of new edges" class="work-thumbnail" src="https://attachments.academia-assets.com/86072912/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317446/Preferential_attachment_with_power_law_growth_in_the_number_of_new_edges">Preferential attachment with power law growth in the number of new edges</a></div><div class="wp-workCard_item"><span>2017 IEEE 56th Annual Conference on Decision and Control (CDC)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Barabasi-Albert model is used to explain the formation of power laws in the degree distributi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The Barabasi-Albert model is used to explain the formation of power laws in the degree distributions of networks. The model assumes that the principle of preferential attachment underlies the growth of networks, that is, new nodes connects to a fixed number of nodes with a probability that is proportional to their degrees. Yet, for empirical networks the number of new edges is often not constant, but varies as more nodes become part of the network. This paper considers an extension to the original Barabasi-Albert model, in which the number of edges established by a new node follows a power law distribution with support in the total number of nodes. While most new nodes connect to a few nodes, some new nodes connect to a larger number. We first characterize the dynamics of growth of the degree of the nodes. Second, we identify sufficient conditions under which the expected value of the average degree of the network is asymptotically stable. Finally, we show how the dynamics of the mo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="41373134a6e2ab2596979f2a1ebecc2a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072912,"asset_id":79317446,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072912/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317446"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317446"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317446; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317446]").text(description); $(".js-view-count[data-work-id=79317446]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317446; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317446']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317446, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "41373134a6e2ab2596979f2a1ebecc2a" } } $('.js-work-strip[data-work-id=79317446]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317446,"title":"Preferential attachment with power law growth in the number of new edges","translated_title":"","metadata":{"abstract":"The Barabasi-Albert model is used to explain the formation of power laws in the degree distributions of networks. 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Finally, we show how the dynamics of the mo...","publisher":"2017 IEEE 56th Annual Conference on Decision and Control (CDC)","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"2017 IEEE 56th Annual Conference on Decision and Control (CDC)"},"translated_abstract":"The Barabasi-Albert model is used to explain the formation of power laws in the degree distributions of networks. The model assumes that the principle of preferential attachment underlies the growth of networks, that is, new nodes connects to a fixed number of nodes with a probability that is proportional to their degrees. Yet, for empirical networks the number of new edges is often not constant, but varies as more nodes become part of the network. This paper considers an extension to the original Barabasi-Albert model, in which the number of edges established by a new node follows a power law distribution with support in the total number of nodes. While most new nodes connect to a few nodes, some new nodes connect to a larger number. We first characterize the dynamics of growth of the degree of the nodes. Second, we identify sufficient conditions under which the expected value of the average degree of the network is asymptotically stable. 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On input RNA sequencing read counts (measured for genotypes under control and treatment conditions) and biological replicates, it outputs a collection of characterized genes, potentially relevant to treatment. Technically, the proposed approach is both a generalization and an extension of WGCNA; its main goal is to identify specific modules in a network of genes after a sequence of normalization and filtering steps. In this work, module detection is achieved by using Hierarchical Link Clustering, which can recognize overlapping communities and thus have more biological meaning given the overlapping regulatory domains of systems that generate co-expression. Additional steps and information are also added to the workflow, where some networks in the intermediate steps are forced to be scale-free...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="97247383124d685c0ecaaeb8f9280fca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072915,"asset_id":79317445,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072915/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317445"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317445"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317445; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317445]").text(description); $(".js-view-count[data-work-id=79317445]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317445; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317445']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317445, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "97247383124d685c0ecaaeb8f9280fca" } } $('.js-work-strip[data-work-id=79317445]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317445,"title":"Using Overlapping Communities and Network Structure for Identifying Reduced Groups of Stress Responsive Genes","translated_title":"","metadata":{"abstract":"This paper proposes a workflow to identify genes responding to a specific treatment in an organism, such as abiotic stresses, a main cause of extensive agricultural production losses worldwide. 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It assumes that, while eigenvectors remain constant, eigenvalues evolve in a predictable manner over time. This paper extends the original formulation of the model twofold. First, it presents a method to compute an approximation of the spectral evolution of eigenvalues based on the Rayleigh quotient. Second, it proposes an algorithm to estimate the evolution of eigenvalues by extrapolating only a fraction of their approximated values. The proposed model is used to characterize mention networks of users who posted tweets that include the most popular political hashtags in Colombia from August 2017 to August 2018 (the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). To evaluate the extent to which the spectral evolution model resembles these networks, link prediction methods based on learning algorithms (i.e., extrapolation and regression) and graph kernels are implemented. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317440"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317440/Stability_properties_of_reciprocal_networks"><img alt="Research paper thumbnail of Stability properties of reciprocal networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317440/Stability_properties_of_reciprocal_networks">Stability properties of reciprocal networks</a></div><div class="wp-workCard_item"><span>2016 American Control Conference (ACC)</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the aims of network formation models is to explain salient properties of empirical network...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One of the aims of network formation models is to explain salient properties of empirical networks based on simple mechanisms for establishing links. Such mechanisms include random attachment (a generic abstraction of how new incoming nodes connect to a network), triadic closure (how the new nodes establish transitive relationships), and network response (how nodes react to new attachments). Our work analyzes the combined effect of the three mechanisms on various local and global network properties. In particular, we derive an expression for the asymptotic behavior of the local reciprocity coefficient as a function of the in-degree of a node. Furthermore, we show that the dynamics of the global reciprocity and the global clustering coefficients correspond to time-varying linear systems. Finally, we identify conditions under which the equilibria of both coefficients are asymptotically stable.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317440"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317440"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317440; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317440]").text(description); $(".js-view-count[data-work-id=79317440]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317440; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317440']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317440, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79317440]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317440,"title":"Stability properties of reciprocal networks","translated_title":"","metadata":{"abstract":"One of the aims of network formation models is to explain salient properties of empirical networks based on simple mechanisms for establishing links. 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Finally, we identify conditions under which the equilibria of both coefficients are asymptotically stable.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"2016 American Control Conference (ACC)"},"translated_abstract":"One of the aims of network formation models is to explain salient properties of empirical networks based on simple mechanisms for establishing links. Such mechanisms include random attachment (a generic abstraction of how new incoming nodes connect to a network), triadic closure (how the new nodes establish transitive relationships), and network response (how nodes react to new attachments). Our work analyzes the combined effect of the three mechanisms on various local and global network properties. In particular, we derive an expression for the asymptotic behavior of the local reciprocity coefficient as a function of the in-degree of a node. Furthermore, we show that the dynamics of the global reciprocity and the global clustering coefficients correspond to time-varying linear systems. Finally, we identify conditions under which the equilibria of both coefficients are asymptotically stable.","internal_url":"https://www.academia.edu/79317440/Stability_properties_of_reciprocal_networks","translated_internal_url":"","created_at":"2022-05-17T09:06:19.868-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":316649,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Stability_properties_of_reciprocal_networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":316649,"first_name":"Jorge","middle_initials":null,"last_name":"Finke","page_name":"Finke","domain_name":"javeriana","created_at":"2011-01-26T04:19:09.875-08:00","display_name":"Jorge Finke","url":"https://javeriana.academia.edu/Finke"},"attachments":[],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":958429,"name":"Reciprocal","url":"https://www.academia.edu/Documents/in/Reciprocal"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="45387" id="papers"><div class="js-work-strip profile--work_container" data-work-id="96156199"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/96156199/The_%C3%93MICAS_alliance_an_international_research_program_on_multi_omics_for_crop_breeding_optimization"><img alt="Research paper thumbnail of The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/98133121/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/96156199/The_%C3%93MICAS_alliance_an_international_research_program_on_multi_omics_for_crop_breeding_optimization">The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization</a></div><div class="wp-workCard_item"><span>Frontiers in Plant Science</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The OMICAS alliance is part of the Colombian government’s Scientific Ecosystem, established betwe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The OMICAS alliance is part of the Colombian government’s Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program’s kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83495729"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83495729/Spectral_Evolution_of_Twitter_Mention_Networks"><img alt="Research paper thumbnail of Spectral Evolution of Twitter Mention Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83495729/Spectral_Evolution_of_Twitter_Mention_Networks">Spectral Evolution of Twitter Mention Networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This papers applies the spectral evolution model presented in [5] to networks of mentions between...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This papers applies the spectral evolution model presented in [5] to networks of mentions between Twitter users who identified messages with the most popular political hashtags in Colombia (during the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). The model characterizes the dynamics of each mention network (i.e., how new edges are established) in terms of the eigen decomposition of its adjacency matrix. It assumes that as new edges are established the eigenvalues change, while the eigenvectors remain constant. The goal of our work is to evaluate various link prediction methods that underlie the spectral evolution model. In particular, we consider prediction methods based on graph kernels and a learning algorithm that tries to estimate the trajectories of the spectrum. 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Our results show that the learning algorithm tends to outperform the kernel methods at predicting the formation of new edges.","publisher":"COMPLEX NETWORKS","publication_date":{"day":null,"month":null,"year":2019,"errors":{}}},"translated_abstract":"This papers applies the spectral evolution model presented in [5] to networks of mentions between Twitter users who identified messages with the most popular political hashtags in Colombia (during the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). The model characterizes the dynamics of each mention network (i.e., how new edges are established) in terms of the eigen decomposition of its adjacency matrix. It assumes that as new edges are established the eigenvalues change, while the eigenvectors remain constant. The goal of our work is to evaluate various link prediction methods that underlie the spectral evolution model. 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Our results show that the learning algorithm tends to outperform the kernel methods at predicting the formation of new edges.","internal_url":"https://www.academia.edu/83495729/Spectral_Evolution_of_Twitter_Mention_Networks","translated_internal_url":"","created_at":"2022-07-21T02:37:29.120-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":316649,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Spectral_Evolution_of_Twitter_Mention_Networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":316649,"first_name":"Jorge","middle_initials":null,"last_name":"Finke","page_name":"Finke","domain_name":"javeriana","created_at":"2011-01-26T04:19:09.875-08:00","display_name":"Jorge Finke","url":"https://javeriana.academia.edu/Finke"},"attachments":[],"research_interests":[],"urls":[{"id":22332383,"url":"https://doi.org/10.1007/978-3-030-36687-2_44"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83495716"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83495716/Detecting_Hotspots_on_Networks"><img alt="Research paper thumbnail of Detecting Hotspots on Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83495716/Detecting_Hotspots_on_Networks">Detecting Hotspots on Networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Traditional approaches for measuring the concentration of events pay little attention to the effe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Traditional approaches for measuring the concentration of events pay little attention to the effects of topological properties. To overcome this limitation, our work develops a theoretical framework to determine whether events are concentrated on a subset of interconnected nodes. We focus on low-clustered networks with regular, Poisson, and power-law degree distributions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83495716"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83495716"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83495716; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83495716]").text(description); $(".js-view-count[data-work-id=83495716]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83495716; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83495716']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83495716, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=83495716]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83495716,"title":"Detecting Hotspots on Networks","translated_title":"","metadata":{"abstract":"Traditional approaches for measuring the concentration of events pay little attention to the effects of topological properties. 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We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. Simulation results illustrate how the number of new edges and the process of reciprocity significantly impact t...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="01d945f1df798a27fc2cd1ef00b98ebc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88818823,"asset_id":83495664,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88818823/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83495664"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83495664"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83495664; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83495664]").text(description); $(".js-view-count[data-work-id=83495664]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83495664; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83495664']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 83495664, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "01d945f1df798a27fc2cd1ef00b98ebc" } } $('.js-work-strip[data-work-id=83495664]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83495664,"title":"Characterizing the head of the degree distributions of growing networks","translated_title":"","metadata":{"abstract":"The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. Simulation results illustrate how the number of new edges and the process of reciprocity significantly impact t...","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"arXiv: Physics and Society"},"translated_abstract":"The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317455"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317455/Anomalous_node_detection_in_networks_with_communities_of_different_size"><img alt="Research paper thumbnail of Anomalous node detection in networks with communities of different size" class="work-thumbnail" src="https://attachments.academia-assets.com/86072976/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317455/Anomalous_node_detection_in_networks_with_communities_of_different_size">Anomalous node detection in networks with communities of different size</a></div><div class="wp-workCard_item"><span>2017 American Control Conference (ACC)</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="273454b186bd68e6293a951df8f13709" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072976,"asset_id":79317455,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072976/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317455"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317455"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317455; 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This includes a representation for (i) the tendency of regular nodes to connect with similar others (i.e., establish homophilic relationships); and (ii) the tendency of anomalous nodes to connect to random targets (i.e., establish random connections across the network). Our approach is motivated by the desire to design scalable strategies for detecting signatures of anomalous behavior, using a formal representation to take into account the evolution of network properties. In particular, we assume that regular nodes are distributed across two communities (of different size), and propose an algorithm that identifies anomalous nodes based on both geometric and spectral measures. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317453"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317453/A_top_down_supervised_learning_approach_to_hierarchical_multi_label_classification_in_networks"><img alt="Research paper thumbnail of A top-down supervised learning approach to hierarchical multi-label classification in networks" class="work-thumbnail" src="https://attachments.academia-assets.com/86072977/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317453/A_top_down_supervised_learning_approach_to_hierarchical_multi_label_classification_in_networks">A top-down supervised learning approach to hierarchical multi-label classification in networks</a></div><div class="wp-workCard_item"><span>Applied Network Science</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Node classification is the task of inferring or predicting missing node attributes from informati...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi-label classification, where the attributes to be inferred can be specified as a strict poset. It is based on a top-down classification approach that addresses hierarchical multi-label classification with supervised learning by building a local classifier per class. The proposed model is showcased with a case study on the prediction of gene functions for Oryza sativa Japonica , a variety of rice. It is compared to the Hierarchical Binomial-Neighborhood, a probabilistic model, by evaluating both approaches in terms of prediction performance and computational cost. The results in this work support the working hypothesis that the proposed model can achieve good levels of prediction efficiency, while scaling up in relation to the state of the art.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="476cb11e96c5af3b49f3d7b7397a424a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072977,"asset_id":79317453,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072977/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317453"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317453"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317453; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317453]").text(description); $(".js-view-count[data-work-id=79317453]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317453; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317453']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317453, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "476cb11e96c5af3b49f3d7b7397a424a" } } $('.js-work-strip[data-work-id=79317453]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317453,"title":"A top-down supervised learning approach to hierarchical multi-label classification in networks","translated_title":"","metadata":{"abstract":"Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. 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Sci.","ai_title_tag":"Top-Down Supervised Learning for Hierarchical Multi-Label Classification","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"Applied Network Science"},"translated_abstract":"Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi-label classification, where the attributes to be inferred can be specified as a strict poset. It is based on a top-down classification approach that addresses hierarchical multi-label classification with supervised learning by building a local classifier per class. The proposed model is showcased with a case study on the prediction of gene functions for Oryza sativa Japonica , a variety of rice. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317452"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317452/Characterizing_the_head_of_the_degrees_distributions_of_growing_networks"><img alt="Research paper thumbnail of Characterizing the head of the degrees distributions of growing networks" class="work-thumbnail" src="https://attachments.academia-assets.com/86072979/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317452/Characterizing_the_head_of_the_degrees_distributions_of_growing_networks">Characterizing the head of the degrees distributions of growing networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The analysis in this paper helps to explain the formation of growing networks with degree distrib...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. Simulation results illustrate how the number of new edges and the process of reciprocity significantly impact t...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a01ea339803b63534df3dc6106a6c2b0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072979,"asset_id":79317452,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072979/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317452"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317452"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317452; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317452]").text(description); $(".js-view-count[data-work-id=79317452]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317452; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317452']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317452, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a01ea339803b63534df3dc6106a6c2b0" } } $('.js-work-strip[data-work-id=79317452]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317452,"title":"Characterizing the head of the degrees distributions of growing networks","translated_title":"","metadata":{"abstract":"The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic model in which edge dynamics are driven by a continuous attachment of new nodes and a mixed attachment mechanism that triggers random or preferential attachment. Furthermore, reciprocal edges to newly added nodes are established according to a response mechanism. The proposed framework extends previous mixed attachment models by allowing the number of new edges to vary according to various discrete probability distributions, including Poisson, Binomial, Zeta, and Log-Series. We derive analytical expressions for the limit in-degree distribution that results from the mixed attachment and response mechanisms. Moreover, we describe the evolution of the dynamics of the cumulative in-degree distribution. 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A number of techniques have been proposed that aim to annotate genes, i.e., identify unknown biological associations between biological processes and genes. The ultimate goal of these techniques is to narrow down the search for promising candidates to carry out further studies through in-vivo experiments. This paper presents an approach for the in-silico prediction of functional gene annotations. It uses existing knowledge body of gene annotations of a given genome and the topological properties of its gene co-expression network, to train a supervised machine learning model that is designed to discover unknown annotations. The approach is applied to Oryza Sativa Japonica (a variety of rice). Our results show that the topological properties help in obtaining a more precise prediction for annotating genes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317451"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317451"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317451; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317451]").text(description); $(".js-view-count[data-work-id=79317451]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317451; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317451']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317451, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79317451]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317451,"title":"In-silico Gene Annotation Prediction Using the Co-expression Network Structure","translated_title":"","metadata":{"abstract":"Identifying which genes are involved in particular biological processes is relevant to understand the structure and function of a genome. 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This paper proposes a network model that explains the emergence of cohesion and homophily as an aggregate outcome at the group- and network-level. We introduce two simple mechanisms that capture the underlying tendencies of nodes to connect with similar and different others. Our main theoretical result presents conditions on the network under which it reaches high degrees of cohesion and homophily.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ed00557d30c44c565334112d4e8287a3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072973,"asset_id":79317450,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072973/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317450"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317450"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317450; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317450]").text(description); $(".js-view-count[data-work-id=79317450]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317450; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317450']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317450, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ed00557d30c44c565334112d4e8287a3" } } $('.js-work-strip[data-work-id=79317450]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317450,"title":"Dynamics of group cohesion in homophilic networks","translated_title":"","metadata":{"abstract":"Understanding cohesion and homophily in empirical networks allows us build better personalization and recommendation systems. 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Our main theoretical result presents conditions on the network under which it reaches high degrees of cohesion and homophily.","internal_url":"https://www.academia.edu/79317450/Dynamics_of_group_cohesion_in_homophilic_networks","translated_internal_url":"","created_at":"2022-05-17T09:06:21.313-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":316649,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":86072973,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86072973/thumbnails/1.jpg","file_name":"8263988.pdf","download_url":"https://www.academia.edu/attachments/86072973/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Dynamics_of_group_cohesion_in_homophilic.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86072973/8263988-libre.pdf?1652803999=\u0026response-content-disposition=attachment%3B+filename%3DDynamics_of_group_cohesion_in_homophilic.pdf\u0026Expires=1733264972\u0026Signature=EwZseWLqd3VpCag7I02HnK8Veeq4oZlGvXV6TAG6C1L6ZNy0y0S~56zvI5MgloPHags23-Cfg2416nWBXFGwALmsbPyYTF0E~njrQzh4wW8Hek6txCUpMdjzKiXPwcidiNI9eVORYtvXdstfC~t5RDizMW0n3n33-hMoxsCCxruosNtPPOCLvQCl36VtXwhJPly7UG9TrfFUqAEcvC92tb6l6nhBZXkIp4Th4IlhpsYGkyhiOxVXKC7iCrXx3GATYimYcL0~TmjqQaLy7uR-GAKmaf9pI9kS-B02q2-TSr6QCP2y7It1okIyu6H1Y4UlnbU0OsuT6VZFZVB0KIvREw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Dynamics_of_group_cohesion_in_homophilic_networks","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":316649,"first_name":"Jorge","middle_initials":null,"last_name":"Finke","page_name":"Finke","domain_name":"javeriana","created_at":"2011-01-26T04:19:09.875-08:00","display_name":"Jorge Finke","url":"https://javeriana.academia.edu/Finke"},"attachments":[{"id":86072973,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86072973/thumbnails/1.jpg","file_name":"8263988.pdf","download_url":"https://www.academia.edu/attachments/86072973/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Dynamics_of_group_cohesion_in_homophilic.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86072973/8263988-libre.pdf?1652803999=\u0026response-content-disposition=attachment%3B+filename%3DDynamics_of_group_cohesion_in_homophilic.pdf\u0026Expires=1733264972\u0026Signature=EwZseWLqd3VpCag7I02HnK8Veeq4oZlGvXV6TAG6C1L6ZNy0y0S~56zvI5MgloPHags23-Cfg2416nWBXFGwALmsbPyYTF0E~njrQzh4wW8Hek6txCUpMdjzKiXPwcidiNI9eVORYtvXdstfC~t5RDizMW0n3n33-hMoxsCCxruosNtPPOCLvQCl36VtXwhJPly7UG9TrfFUqAEcvC92tb6l6nhBZXkIp4Th4IlhpsYGkyhiOxVXKC7iCrXx3GATYimYcL0~TmjqQaLy7uR-GAKmaf9pI9kS-B02q2-TSr6QCP2y7It1okIyu6H1Y4UlnbU0OsuT6VZFZVB0KIvREw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":111436,"name":"IEEE","url":"https://www.academia.edu/Documents/in/IEEE"}],"urls":[{"id":20535000,"url":"https://doi.org/10.1109/CDC.2017.8263988"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317449"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317449/Dynamics_of_degree_distributions_of_social_networks"><img alt="Research paper thumbnail of Dynamics of degree distributions of social networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317449/Dynamics_of_degree_distributions_of_social_networks">Dynamics of degree distributions of social networks</a></div><div class="wp-workCard_item"><span>2017 IEEE 56th Annual Conference on Decision and Control (CDC)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Social network models aim to capture the complex structure of social connections. They are a fram...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Social network models aim to capture the complex structure of social connections. They are a framework for the design of control algorithms that take into account relationships, interactions, and communications between social actors. Based on three formation mechanisms — random attachment, triad formation, and network response — our work characterizes the dynamics of the degree distributions of social networks. In particular, we show that the complementary cumulative in- and out-degree distributions of highly clustered, reciprocal networks can be approximated by infinite dimensional time-varying linear systems. Furthermore, we determine the invariance of both limit distributions and the stability properties of the average degree.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317449"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317449"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317449; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317449]").text(description); $(".js-view-count[data-work-id=79317449]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317449; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317449']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317449, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79317449]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317449,"title":"Dynamics of degree distributions of social networks","translated_title":"","metadata":{"abstract":"Social network models aim to capture the complex structure of social connections. 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Furthermore, we determine the invariance of both limit distributions and the stability properties of the average degree.","internal_url":"https://www.academia.edu/79317449/Dynamics_of_degree_distributions_of_social_networks","translated_internal_url":"","created_at":"2022-05-17T09:06:21.159-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":316649,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Dynamics_of_degree_distributions_of_social_networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":316649,"first_name":"Jorge","middle_initials":null,"last_name":"Finke","page_name":"Finke","domain_name":"javeriana","created_at":"2011-01-26T04:19:09.875-08:00","display_name":"Jorge Finke","url":"https://javeriana.academia.edu/Finke"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":20534999,"url":"http://xplorestaging.ieee.org/ielx7/8253407/8263624/08264406.pdf?arnumber=8264406"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317448"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317448/A_Random_Network_Model_for_the_Analysis_of_Blockchain_Designs_with_Communication_Delay"><img alt="Research paper thumbnail of A Random Network Model for the Analysis of Blockchain Designs with Communication Delay" class="work-thumbnail" src="https://attachments.academia-assets.com/86072914/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317448/A_Random_Network_Model_for_the_Analysis_of_Blockchain_Designs_with_Communication_Delay">A Random Network Model for the Analysis of Blockchain Designs with Communication Delay</a></div><div class="wp-workCard_item"><span>ArXiv</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper proposes a random network model for blockchains, a distributed hierarchical data struc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper proposes a random network model for blockchains, a distributed hierarchical data structure of blocks that has found several applications in various industries. The model is parametric on two probability distribution functions governing block production and communication delay, which are key to capture the complexity of the mechanism used to synchronize the many distributed local copies of a blockchain. The proposed model is equipped with simulation algorithms for both bounded and unbounded number of distributed copies of the blockchain. They are used to study fast blockchain systems, i.e., blockchains in which the average time of block production can match the average time of message broadcasting used for blockchain synchronization. In particular, the model and the algorithms are useful to understand efficiency criteria associated with fast blockchains for identifying, e.g., when increasing the block production will have negative impact on the stability of the distributed...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="96fa3588541396de21a710275ed3202a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072914,"asset_id":79317448,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072914/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317448"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317448"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317448; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317448]").text(description); $(".js-view-count[data-work-id=79317448]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317448; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317448']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317448, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "96fa3588541396de21a710275ed3202a" } } $('.js-work-strip[data-work-id=79317448]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317448,"title":"A Random Network Model for the Analysis of Blockchain Designs with Communication Delay","translated_title":"","metadata":{"abstract":"This paper proposes a random network model for blockchains, a distributed hierarchical data structure of blocks that has found several applications in various industries. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317447"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317447/J_un_2_01_2_Power_law_weighted_networks_from_local_attachments"><img alt="Research paper thumbnail of J un 2 01 2 Power-law weighted networks from local attachments" class="work-thumbnail" src="https://attachments.academia-assets.com/86072913/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317447/J_un_2_01_2_Power_law_weighted_networks_from_local_attachments">J un 2 01 2 Power-law weighted networks from local attachments</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This letter introduces a mechanism for constructing, through a process of distributed decision-ma...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This letter introduces a mechanism for constructing, through a process of distributed decision-making, substrates for the study of collective dynamics on extended power-law weighted networks with both a desired scaling exponent and a fixed clustering coefficient. The analytical results show that the connectivity distribution converges to the scaling behavior often found in social and engineering systems. To illustrate the approach of the proposed framework we generate network substrates that resemble steady state properties of the empirical citation distributions of (i) publications indexed by the Institute for Scientific Information from 1981 to 1997; (ii) patents granted by the U.S. Patent and Trademark Office from 1975 to 1999; and (iii) opinions written by the Supreme Court and the cases they cite from 1754 to 2002.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="737a075284ce1dbdc144dc6de6da98c3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072913,"asset_id":79317447,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072913/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317447"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317447"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317447; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317447]").text(description); $(".js-view-count[data-work-id=79317447]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317447; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317447']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317447, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "737a075284ce1dbdc144dc6de6da98c3" } } $('.js-work-strip[data-work-id=79317447]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317447,"title":"J un 2 01 2 Power-law weighted networks from local attachments","translated_title":"","metadata":{"abstract":"This letter introduces a mechanism for constructing, through a process of distributed decision-making, substrates for the study of collective dynamics on extended power-law weighted networks with both a desired scaling exponent and a fixed clustering coefficient. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317446"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317446/Preferential_attachment_with_power_law_growth_in_the_number_of_new_edges"><img alt="Research paper thumbnail of Preferential attachment with power law growth in the number of new edges" class="work-thumbnail" src="https://attachments.academia-assets.com/86072912/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317446/Preferential_attachment_with_power_law_growth_in_the_number_of_new_edges">Preferential attachment with power law growth in the number of new edges</a></div><div class="wp-workCard_item"><span>2017 IEEE 56th Annual Conference on Decision and Control (CDC)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Barabasi-Albert model is used to explain the formation of power laws in the degree distributi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The Barabasi-Albert model is used to explain the formation of power laws in the degree distributions of networks. The model assumes that the principle of preferential attachment underlies the growth of networks, that is, new nodes connects to a fixed number of nodes with a probability that is proportional to their degrees. Yet, for empirical networks the number of new edges is often not constant, but varies as more nodes become part of the network. This paper considers an extension to the original Barabasi-Albert model, in which the number of edges established by a new node follows a power law distribution with support in the total number of nodes. While most new nodes connect to a few nodes, some new nodes connect to a larger number. We first characterize the dynamics of growth of the degree of the nodes. Second, we identify sufficient conditions under which the expected value of the average degree of the network is asymptotically stable. Finally, we show how the dynamics of the mo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="41373134a6e2ab2596979f2a1ebecc2a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072912,"asset_id":79317446,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072912/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317446"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317446"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317446; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317446]").text(description); $(".js-view-count[data-work-id=79317446]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317446; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317446']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317446, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "41373134a6e2ab2596979f2a1ebecc2a" } } $('.js-work-strip[data-work-id=79317446]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317446,"title":"Preferential attachment with power law growth in the number of new edges","translated_title":"","metadata":{"abstract":"The Barabasi-Albert model is used to explain the formation of power laws in the degree distributions of networks. The model assumes that the principle of preferential attachment underlies the growth of networks, that is, new nodes connects to a fixed number of nodes with a probability that is proportional to their degrees. Yet, for empirical networks the number of new edges is often not constant, but varies as more nodes become part of the network. This paper considers an extension to the original Barabasi-Albert model, in which the number of edges established by a new node follows a power law distribution with support in the total number of nodes. While most new nodes connect to a few nodes, some new nodes connect to a larger number. We first characterize the dynamics of growth of the degree of the nodes. Second, we identify sufficient conditions under which the expected value of the average degree of the network is asymptotically stable. Finally, we show how the dynamics of the mo...","publisher":"2017 IEEE 56th Annual Conference on Decision and Control (CDC)","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"2017 IEEE 56th Annual Conference on Decision and Control (CDC)"},"translated_abstract":"The Barabasi-Albert model is used to explain the formation of power laws in the degree distributions of networks. The model assumes that the principle of preferential attachment underlies the growth of networks, that is, new nodes connects to a fixed number of nodes with a probability that is proportional to their degrees. Yet, for empirical networks the number of new edges is often not constant, but varies as more nodes become part of the network. This paper considers an extension to the original Barabasi-Albert model, in which the number of edges established by a new node follows a power law distribution with support in the total number of nodes. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317445"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317445/Using_Overlapping_Communities_and_Network_Structure_for_Identifying_Reduced_Groups_of_Stress_Responsive_Genes"><img alt="Research paper thumbnail of Using Overlapping Communities and Network Structure for Identifying Reduced Groups of Stress Responsive Genes" class="work-thumbnail" src="https://attachments.academia-assets.com/86072915/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317445/Using_Overlapping_Communities_and_Network_Structure_for_Identifying_Reduced_Groups_of_Stress_Responsive_Genes">Using Overlapping Communities and Network Structure for Identifying Reduced Groups of Stress Responsive Genes</a></div><div class="wp-workCard_item"><span>ArXiv</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper proposes a workflow to identify genes responding to a specific treatment in an organis...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper proposes a workflow to identify genes responding to a specific treatment in an organism, such as abiotic stresses, a main cause of extensive agricultural production losses worldwide. On input RNA sequencing read counts (measured for genotypes under control and treatment conditions) and biological replicates, it outputs a collection of characterized genes, potentially relevant to treatment. Technically, the proposed approach is both a generalization and an extension of WGCNA; its main goal is to identify specific modules in a network of genes after a sequence of normalization and filtering steps. In this work, module detection is achieved by using Hierarchical Link Clustering, which can recognize overlapping communities and thus have more biological meaning given the overlapping regulatory domains of systems that generate co-expression. Additional steps and information are also added to the workflow, where some networks in the intermediate steps are forced to be scale-free...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="97247383124d685c0ecaaeb8f9280fca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072915,"asset_id":79317445,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072915/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317445"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317445"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317445; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79317445]").text(description); $(".js-view-count[data-work-id=79317445]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79317445; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79317445']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79317445, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "97247383124d685c0ecaaeb8f9280fca" } } $('.js-work-strip[data-work-id=79317445]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79317445,"title":"Using Overlapping Communities and Network Structure for Identifying Reduced Groups of Stress Responsive Genes","translated_title":"","metadata":{"abstract":"This paper proposes a workflow to identify genes responding to a specific treatment in an organism, such as abiotic stresses, a main cause of extensive agricultural production losses worldwide. 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It assumes that, while eigenvectors remain constant, eigenvalues evolve in a predictable manner over time. This paper extends the original formulation of the model twofold. First, it presents a method to compute an approximation of the spectral evolution of eigenvalues based on the Rayleigh quotient. Second, it proposes an algorithm to estimate the evolution of eigenvalues by extrapolating only a fraction of their approximated values. The proposed model is used to characterize mention networks of users who posted tweets that include the most popular political hashtags in Colombia from August 2017 to August 2018 (the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). To evaluate the extent to which the spectral evolution model resembles these networks, link prediction methods based on learning algorithms (i.e., extrapolation and regression) and graph kernels are implemented. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317442"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317442/Community_Based_Event_Detection_in_Temporal_Networks"><img alt="Research paper thumbnail of Community-Based Event Detection in Temporal Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/86072918/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317442/Community_Based_Event_Detection_in_Temporal_Networks">Community-Based Event Detection in Temporal Networks</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="dced003df792caeb36e127533724b081" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86072918,"asset_id":79317442,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86072918/download_file?st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&st=MTczMzI2MTM3Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79317442"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79317442"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79317442; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79317440"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79317440/Stability_properties_of_reciprocal_networks"><img alt="Research paper thumbnail of Stability properties of reciprocal networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79317440/Stability_properties_of_reciprocal_networks">Stability properties of reciprocal networks</a></div><div class="wp-workCard_item"><span>2016 American Control Conference (ACC)</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the aims of network formation models is to explain salient properties of empirical network...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One of the aims of network formation models is to explain salient properties of empirical networks based on simple mechanisms for establishing links. Such mechanisms include random attachment (a generic abstraction of how new incoming nodes connect to a network), triadic closure (how the new nodes establish transitive relationships), and network response (how nodes react to new attachments). Our work analyzes the combined effect of the three mechanisms on various local and global network properties. In particular, we derive an expression for the asymptotic behavior of the local reciprocity coefficient as a function of the in-degree of a node. Furthermore, we show that the dynamics of the global reciprocity and the global clustering coefficients correspond to time-varying linear systems. 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