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(PDF) A system for building immunity in social networks
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Rumors often spread rapidly through the network and, if not contained quickly, can be harmful. This paper describes a method for identifying" /> <meta name="twitter:image" content="https://0.academia-photos.com/26678070/8232471/15250726/s200_heena.rathore.jpg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/11023974/A_system_for_building_immunity_in_social_networks" /> <meta property="og:title" content="A system for building immunity in social networks" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="Social networks are susceptible to rapid spread of malicious information, commonly referred to as rumors. Rumors often spread rapidly through the network and, if not contained quickly, can be harmful. 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"https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F11023974%2FA_system_for_building_immunity_in_social_networks%3Fshow_translation%3Dtrue"; window.loswp.previewableAttachments = [{"id":37194872,"identifier":"Attachment_37194872","shouldShowBulkDownload":false}]; window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":11023974,"created_at":"2015-02-23T13:40:52.218-08:00","from_world_paper_id":131905143,"updated_at":"2024-11-16T13:43:58.522-08:00","_data":{"grobid_abstract":"Social networks are susceptible to rapid spread of malicious information, commonly referred to as rumors. Rumors often spread rapidly through the network and, if not contained quickly, can be harmful. This paper describes a method for identifying highly connected nodes in a social network and using these nodes to build immunity against such malicious information. To describe this method, this paper draws inspiration from two well established topics in the area of biology; spread of communicable diseases in human population and how human body builds immunity against diseases. In case of communicable diseases, it would be very simplistic if we only consider that an infected node can transmit its disease to its nearest neighbors. More realistically speaking, it is possible that an infected node can develop random links with other nodes in the system. The spread of communicable diseases is controlled by both these factors. An infected node with capability to have several random links is capable of spreading the disease through the network faster. We postulate that certain nodes in a social network exhibit similar behavior and can be defined as highly connected nodes in the network. We present analytical tools based on our network simulation, to correctly identify such nodes. Once such nodes are identified, we introduce the concept of weighting functions that can be attached to messages passing through such nodes. This paper describes how the spread of malicious information can be controlled by a community of such highly connected nodes, using the concept of weighted functions.","publication_date":"2012,,","publication_name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","grobid_abstract_attachment_id":"37194872"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A system for building immunity in social networks","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [26678070]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card ds-work-card--no-bottom-spacing"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":37194872,"attachmentType":"pdf"}"><img alt="First page of “A system for building immunity in social networks”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/37194872/mini_magick20220711-26877-892cm5.png?1657608469" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">A system for building immunity in social networks</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="26678070" href="https://iitj.academia.edu/HeenaRathore"><img alt="Profile image of Heena Rathore" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/26678070/8232471/15250726/s65_heena.rathore.jpg" />Heena Rathore</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2012, 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">5 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 11023974; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Social networks are susceptible to rapid spread of malicious information, commonly referred to as rumors. Rumors often spread rapidly through the network and, if not contained quickly, can be harmful. This paper describes a method for identifying highly connected nodes in a social network and using these nodes to build immunity against such malicious information. To describe this method, this paper draws inspiration from two well established topics in the area of biology; spread of communicable diseases in human population and how human body builds immunity against diseases. In case of communicable diseases, it would be very simplistic if we only consider that an infected node can transmit its disease to its nearest neighbors. More realistically speaking, it is possible that an infected node can develop random links with other nodes in the system. The spread of communicable diseases is controlled by both these factors. An infected node with capability to have several random links is capable of spreading the disease through the network faster. We postulate that certain nodes in a social network exhibit similar behavior and can be defined as highly connected nodes in the network. We present analytical tools based on our network simulation, to correctly identify such nodes. Once such nodes are identified, we introduce the concept of weighting functions that can be attached to messages passing through such nodes. 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