Social and Information Networks

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href="/list/cs.SI/recent">recent</a> articles</p> <h3>Showing new listings for Thursday, 28 November 2024</h3> <div class='paging'>Total of 9 entries </div> <div class='morefewer'>Showing up to 2000 entries per page: <a href=/list/cs.SI/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> <dl id='articles'> <h3>Cross submissions (showing 3 of 3 entries)</h3> <dt> <a name='item1'>[1]</a> <a href ="/abs/2411.18085" title="Abstract" id="2411.18085"> arXiv:2411.18085 </a> (cross-list from cs.AI) [<a href="/pdf/2411.18085" title="Download PDF" id="pdf-2411.18085" aria-labelledby="pdf-2411.18085">pdf</a>, <a href="https://arxiv.org/html/2411.18085v1" title="View HTML" id="html-2411.18085" aria-labelledby="html-2411.18085" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18085" title="Other formats" id="oth-2411.18085" aria-labelledby="oth-2411.18085">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Fan,+M">Miao Fan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+J">Jizhou Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhuo,+A">An Zhuo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Y">Ying Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+P">Ping Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+H">Haifeng Wang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> CIKM'19 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Artificial Intelligence (cs.AI)</span>; Social and Information Networks (cs.SI) </div> <p class='mathjax'> The value assessment of private properties is an attractive but challenging task which is widely concerned by a majority of people around the world. A prolonged topic among us is ``\textit{how much is my house worth?}''. To answer this question, most experienced agencies would like to price a property given the factors of its attributes as well as the demographics and the public facilities around it. However, no one knows the exact prices of these factors, especially the values of public facilities which may help assess private properties. In this paper, we introduce our newly launched project ``Monopoly'' (named after a classic board game) in which we propose a distributed approach for revaluing private properties by learning to price public facilities (such as hospitals etc.) with the large-scale urban data we have accumulated via Baidu Maps. To be specific, our method organizes many points of interest (POIs) into an undirected weighted graph and formulates multiple factors including the virtual prices of surrounding public facilities as adaptive variables to parallelly estimate the housing prices we know. Then the prices of both public facilities and private properties can be iteratively updated according to the loss of prediction until convergence. We have conducted extensive experiments with the large-scale urban data of several metropolises in China. Results show that our approach outperforms several mainstream methods with significant margins. Further insights from more in-depth discussions demonstrate that the ``Monopoly'' is an innovative application in the interdisciplinary field of business intelligence and urban computing, and it will be beneficial to tens of millions of our users for investments and to the governments for urban planning as well as taxation. </p> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2411.18383" title="Abstract" id="2411.18383"> arXiv:2411.18383 </a> (cross-list from cs.CL) [<a href="/pdf/2411.18383" title="Download PDF" id="pdf-2411.18383" aria-labelledby="pdf-2411.18383">pdf</a>, <a href="https://arxiv.org/html/2411.18383v1" title="View HTML" id="html-2411.18383" aria-labelledby="html-2411.18383" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18383" title="Other formats" id="oth-2411.18383" aria-labelledby="oth-2411.18383">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Topic Modeling and Sentiment Analysis on Japanese Online Media's Coverage of Nuclear Energy </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+Y">Yifan Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tsuruta,+H">Hirofumi Tsuruta</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kumagai,+M">Masaya Kumagai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kurosaki,+K">Ken Kurosaki</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 15 pages, 9 figures, 4 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computation and Language (cs.CL)</span>; Social and Information Networks (cs.SI) </div> <p class='mathjax'> Thirteen years after the Fukushima Daiichi nuclear power plant accident, Japan's nuclear energy accounts for only approximately 6% of electricity production, as most nuclear plants remain shut down. To revitalize the nuclear industry and achieve sustainable development goals, effective communication with Japanese citizens, grounded in an accurate understanding of public sentiment, is of paramount importance. While nationwide surveys have traditionally been used to gauge public views, the rise of social media in recent years has provided a promising new avenue for understanding public sentiment. To explore domestic sentiment on nuclear energy-related issues expressed online, we analyzed the content and comments of over 3,000 YouTube videos covering topics related to nuclear energy. Topic modeling was used to extract the main topics from the videos, and sentiment analysis with large language models classified user sentiments towards each topic. Additionally, word co-occurrence network analysis was performed to examine the shift in online discussions during August and September 2023 regarding the release of treated water. Overall, our results provide valuable insights into the online discourse on nuclear energy and contribute to a more comprehensive understanding of public sentiment in Japan. </p> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2411.18614" title="Abstract" id="2411.18614"> arXiv:2411.18614 </a> (cross-list from cs.DS) [<a href="/pdf/2411.18614" title="Download PDF" id="pdf-2411.18614" aria-labelledby="pdf-2411.18614">pdf</a>, <a href="https://arxiv.org/html/2411.18614v1" title="View HTML" id="html-2411.18614" aria-labelledby="html-2411.18614" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18614" title="Other formats" id="oth-2411.18614" aria-labelledby="oth-2411.18614">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Optimal root recovery for uniform attachment trees and $d$-regular growing trees </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Addario-Berry,+L">Louigi Addario-Berry</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fontaine,+C">Catherine Fontaine</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Khanfir,+R">Robin Khanfir</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Langevin,+L">Louis-Roy Langevin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=T%C3%AAtu,+S">Simone T锚tu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 27 pages, 1 figure </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Data Structures and Algorithms (cs.DS)</span>; Social and Information Networks (cs.SI); Probability (math.PR); Statistics Theory (math.ST) </div> <p class='mathjax'> We consider root-finding algorithms for random rooted trees grown by uniform attachment. Given an unlabeled copy of the tree and a target accuracy $\varepsilon > 0$, such an algorithm outputs a set of nodes that contains the root with probability at least $1 - \varepsilon$. We prove that, for the optimal algorithm, an output set of size $\exp(O(\log^{1/2}(1/\varepsilon)))$ suffices; this bound is sharp and answers a question of Bubeck, Devroye and Lugosi (2017). We prove similar bounds for random regular trees that grow by uniform attachment, strengthening a result of Khim and Loh (2017). </p> </div> </dd> </dl> <dl id='articles'> <h3>Replacement submissions (showing 6 of 6 entries)</h3> <dt> <a name='item4'>[4]</a> <a href ="/abs/2305.02371" title="Abstract" id="2305.02371"> arXiv:2305.02371 </a> (replaced) [<a href="/pdf/2305.02371" title="Download PDF" id="pdf-2305.02371" aria-labelledby="pdf-2305.02371">pdf</a>, <a href="https://arxiv.org/html/2305.02371v2" title="View HTML" id="html-2305.02371" aria-labelledby="html-2305.02371" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2305.02371" title="Other formats" id="oth-2305.02371" aria-labelledby="oth-2305.02371">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Spectral influence in networks: An application to Input-Output analysis </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Riane,+N">Nizar Riane</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Social and Information Networks (cs.SI)</span>; Combinatorics (math.CO) </div> <p class='mathjax'> This paper introduces the concepts of spectral influence and spectral cyclicality, both derived from the largest eigenvalue of a graph's adjacency matrix. These two novel centrality measures capture both diffusion and interdependence from a local and global perspective respectively. We propose a new clustering algorithm that identifies communities with high cyclicality and interdependence, allowing for overlaps. To illustrate our method, we apply it to input-output analysis within the context of the Moroccan economy. </p> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2404.08098" title="Abstract" id="2404.08098"> arXiv:2404.08098 </a> (replaced) [<a href="/pdf/2404.08098" title="Download PDF" id="pdf-2404.08098" aria-labelledby="pdf-2404.08098">pdf</a>, <a href="/format/2404.08098" title="Other formats" id="oth-2404.08098" aria-labelledby="oth-2404.08098">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The Impact of School and Family Networks on COVID-19 Infections Among Dutch Students: A Study Using Population-Level Registry Data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Garcia-Bernardo,+J">Javier Garcia-Bernardo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Westernhagen,+C+H">Christine Hedde-von Westernhagen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Emery,+T">Tom Emery</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=van+Hoek,+A+J">Albert Jan van Hoek</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Social and Information Networks (cs.SI)</span>; Computers and Society (cs.CY); Physics and Society (physics.soc-ph) </div> <p class='mathjax'> Understanding the impact of different social interactions is key to improving epidemic models. Here, we use extensive registry data -- including PCR test results and population-level networks -- to investigate the impact of school, family, and other social contacts on SARS-CoV-2 transmission in the Netherlands (June 2020--October 2021). We isolate and compare different contexts of potential SARS-CoV-2 transmission by matching pairs of students based on their attendance at the same or different primary school (in 2020) and secondary school (in 2021) and their geographic proximity. We then calculated the probability of temporally associated infections -- i.e. the probability of both students testing positive within a 14-day period. <br>Our results highlight the relative importance of household and family transmission in the spread of SARS-CoV-2 compared to school settings. The probability of temporally associated infections for siblings and parent-child pairs living in the same household was 22.6--23.2\%, and 4.7--7.9\% for family members living in different household. In contrast, the probability of temporally associated infections was 0.52\% for pairs of students living nearby but not attending the same primary or secondary school, 0.66\% for pairs attending different secondary schools but having attended the same primary school, and 1.65\% for pairs attending the same secondary school. Finally, we used multilevel regression analyses to examine how individual, school, and geographic factors contribute to transmission risk. We found that the largest differences in transmission probabilities were due to unobserved individual (60\%) and school-level (35\%) factors. Only a small proportion (3\%) could be attributed to geographic proximity of students or to school size, denomination, or the median income of the school area. </p> </div> </dd> <dt> <a name='item6'>[6]</a> <a href ="/abs/2404.10468" title="Abstract" id="2404.10468"> arXiv:2404.10468 </a> (replaced) [<a href="/pdf/2404.10468" title="Download PDF" id="pdf-2404.10468" aria-labelledby="pdf-2404.10468">pdf</a>, <a href="/format/2404.10468" title="Other formats" id="oth-2404.10468" aria-labelledby="oth-2404.10468">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Community detection and anomaly prediction in dynamic networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Safdari,+H">Hadiseh Safdari</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=De+Bacco,+C">Caterina De Bacco</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Social and Information Networks (cs.SI)</span> </div> <p class='mathjax'> Anomaly detection is an essential task in the analysis of dynamic networks, offering early warnings of abnormal behavior. We present a principled approach to detect anomalies in dynamic networks that integrates community structure as a foundational model for regular behavior. Our model identifies anomalies as irregular edges while capturing structural changes. Our approach leverages a Markovian framework for temporal transitions and latent variables for community and anomaly detection, inferring hidden parameters to detect unusual interactions. Evaluations on synthetic and real-world datasets show strong anomaly detection across various scenarios. In a case study on professional football player transfers, we detect patterns influenced by club wealth and country, as well as unexpected transactions both within and across community boundaries. This work provides a framework for adaptable anomaly detection, highlighting the value of integrating domain knowledge with data-driven techniques for improved interpretability and robustness in complex networks. </p> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2311.10270" title="Abstract" id="2311.10270"> arXiv:2311.10270 </a> (replaced) [<a href="/pdf/2311.10270" title="Download PDF" id="pdf-2311.10270" aria-labelledby="pdf-2311.10270">pdf</a>, <a href="https://arxiv.org/html/2311.10270v5" title="View HTML" id="html-2311.10270" aria-labelledby="html-2311.10270" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2311.10270" title="Other formats" id="oth-2311.10270" aria-labelledby="oth-2311.10270">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Multiscale Hodge Scattering Networks for Data Analysis </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Saito,+N">Naoki Saito</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schonsheck,+S+C">Stefan C. Schonsheck</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shvarts,+E">Eugene Shvarts</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 20 Pages, Comments Welcome </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Social and Information Networks (cs.SI); Signal Processing (eess.SP); Numerical Analysis (math.NA); Machine Learning (stat.ML) </div> <p class='mathjax'> We propose new scattering networks for signals measured on simplicial complexes, which we call \emph{Multiscale Hodge Scattering Networks} (MHSNs). Our construction is based on multiscale basis dictionaries on simplicial complexes, i.e., the $\kappa$-GHWT and $\kappa$-HGLET, which we recently developed for simplices of dimension $\kappa \in \mathbb{N}$ in a given simplicial complex by generalizing the node-based Generalized Haar-Walsh Transform (GHWT) and Hierarchical Graph Laplacian Eigen Transform (HGLET). The $\kappa$-GHWT and the $\kappa$-HGLET both form redundant sets (i.e., dictionaries) of multiscale basis vectors and the corresponding expansion coefficients of a given signal. Our MHSNs use a layered structure analogous to a convolutional neural network (CNN) to cascade the moments of the modulus of the dictionary coefficients. The resulting features are invariant to reordering of the simplices (i.e., node permutation of the underlying graphs). Importantly, the use of multiscale basis dictionaries in our MHSNs admits a natural pooling operation that is akin to local pooling in CNNs, and which may be performed either locally or per-scale. These pooling operations are harder to define in both traditional scattering networks based on Morlet wavelets, and geometric scattering networks based on Diffusion Wavelets. As a result, we are able to extract a rich set of descriptive yet robust features that can be used along with very simple machine learning methods (i.e., logistic regression or support vector machines) to achieve high-accuracy classification systems with far fewer parameters to train than most modern graph neural networks. Finally, we demonstrate the usefulness of our MHSNs in three distinct types of problems: signal classification, domain (i.e., graph/simplex) classification, and molecular dynamics prediction. </p> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2401.15479" title="Abstract" id="2401.15479"> arXiv:2401.15479 </a> (replaced) [<a href="/pdf/2401.15479" title="Download PDF" id="pdf-2401.15479" aria-labelledby="pdf-2401.15479">pdf</a>, <a href="https://arxiv.org/html/2401.15479v4" title="View HTML" id="html-2401.15479" aria-labelledby="html-2401.15479" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2401.15479" title="Other formats" id="oth-2401.15479" aria-labelledby="oth-2401.15479">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Navigating the Post-API Dilemma | Search Engine Results Pages Present a Biased View of Social Media Data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Poudel,+A">Amrit Poudel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Weninger,+T">Tim Weninger</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Proceedings of the ACM Web Conference 2024 (WWW '24) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Information Retrieval (cs.IR)</span>; Computation and Language (cs.CL); Social and Information Networks (cs.SI) </div> <p class='mathjax'> Recent decisions to discontinue access to social media APIs are having detrimental effects on Internet research and the field of computational social science as a whole. This lack of access to data has been dubbed the Post-API era of Internet research. Fortunately, popular search engines have the means to crawl, capture, and surface social media data on their Search Engine Results Pages (SERP) if provided the proper search query, and may provide a solution to this dilemma. In the present work we ask: does SERP provide a complete and unbiased sample of social media data? Is SERP a viable alternative to direct API-access? To answer these questions, we perform a comparative analysis between (Google) SERP results and nonsampled data from Reddit and Twitter/X. We find that SERP results are highly biased in favor of popular posts; against political, pornographic, and vulgar posts; are more positive in their sentiment; and have large topical gaps. Overall, we conclude that SERP is not a viable alternative to social media API access. </p> </div> </dd> <dt> <a name='item9'>[9]</a> <a href ="/abs/2409.12420" title="Abstract" id="2409.12420"> arXiv:2409.12420 </a> (replaced) [<a href="/pdf/2409.12420" title="Download PDF" id="pdf-2409.12420" aria-labelledby="pdf-2409.12420">pdf</a>, <a href="https://arxiv.org/html/2409.12420v2" title="View HTML" id="html-2409.12420" aria-labelledby="html-2409.12420" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2409.12420" title="Other formats" id="oth-2409.12420" aria-labelledby="oth-2409.12420">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Spatially-invariant opinion dynamics on the circle </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Amorim,+G">Giovanna Amorim</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Bizyaeva,+A">Anastasia Bizyaeva</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Franci,+A">Alessio Franci</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Leonard,+N+E">Naomi Ehrich Leonard</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Analysis of PDEs (math.AP)</span>; Social and Information Networks (cs.SI) </div> <p class='mathjax'> We propose and analyze a nonlinear opinion dynamics model for an agent making decisions about a continuous distribution of options in the presence of input. Inspired by perceptual decision-making, we develop new theory for opinion formation in response to inputs about options distributed on the circle. Options on the circle can represent, e.g., the possible directions of perceived objects and resulting heading directions in planar robotic navigation problems. Interactions among options are encoded through a spatially invariant kernel, which we design to ensure that only a small (finite) subset of options can be favored over the continuum. We leverage the spatial invariance of the model linearization to design flexible, distributed opinion-forming behaviors using spatiotemporal frequency domain and bifurcation analysis. We illustrate our model's versatility with an application to robotic navigation in crowded spaces. </p> </div> </dd> </dl> <div class='paging'>Total of 9 entries </div> <div class='morefewer'>Showing up to 2000 entries per page: <a href=/list/cs.SI/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> </div> </div> </div> </main> <footer style="clear: both;"> <div class="columns is-desktop" role="navigation" aria-label="Secondary" style="margin: -0.75em -0.75em 0.75em -0.75em">  <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; 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