Beyond Content: How Author Network Centrality Drives Citation Disparities in Top AI Conferences

Beyond Content: How Author Network Centrality Drives Citation Disparities in Top AI Conferences | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Beyond Content: How Author Network Centrality Drives Citation Disparities in Top AI Conferences RENLONG JIE, Chen Chu, Longfeng Zhao, Danyang Jia, Zhen Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8791539/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates how authors' structural positions within scientific collaboration networks drive citation disparities beyond research content. We analyze 17,942 papers from three top AI conferences (NeurIPS, ICLR and ICML) spanning 2005 to 2024. To address the limitations of existing metrics, we introduce a novel centrality measure, HCTCD, which incorporates temporal decay and collaboration intensity. To isolate the effect of network structure, we rigorously control for textual content via a pre-trained transformer encoder. The relationship is then modeled using Beta regression, with the citation percentile within each publication year as the dependent variable. Our results demonstrate that long-term centrality exerts a stronger influence than short-term metrics. Critically, team-level centrality aggregation outperforms author-rank approaches. Furthermore, after controlling for content and other variables, incorporating a co-author with centrality 50% higher than the first author is associated with a significant increase in expected citations, illustrating a clear “social lift”. These findings underscore the primacy of collective network connectivity and advocate for network-aware evaluation to mitigate structural inequities in scientific recognition. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8791539","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592816263,"identity":"fe25bf3a-3bc2-4017-8d76-69df91268094","order_by":0,"name":"RENLONG JIE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACxmYwZQHEzAegYglEaZEAYjaYUgJaoACkhceAOC3M7czHPnzcISFnzr/m22PeHYcZ+NlzDBh+7sDnMLbkmTPPSBhbzni73Zj3zGEGyZ43Boy9Z/Bp4TFm5m2TSNxw4+w26dy2wwwGN3IMmBnb8Gnh/8z8F6zlzDOwFnvCWniYgQqAWs73sEFskSCohc2YsbdNwtjgBpuZ9N8z6TwSZ54VHOzFo8Ww//Bjhp9tNnIG5w8/k5y5w1qOvz1544Of+LQ0wFgSCUA7Gxh4QOwDuDUwMMjDWfwHwFpGwSgYBaNgFGAAALgITk/Rbfg/AAAAAElFTkSuQmCC","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":true,"prefix":"","firstName":"RENLONG","middleName":"","lastName":"JIE","suffix":""},{"id":592816264,"identity":"4c59fb4f-3f8a-4a16-b653-221093cbee77","order_by":1,"name":"Chen Chu","email":"","orcid":"","institution":"Yunnan University of Finance And Economics","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Chu","suffix":""},{"id":592816268,"identity":"95ca7c3c-2765-4742-b225-123c15698ebb","order_by":2,"name":"Longfeng Zhao","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Longfeng","middleName":"","lastName":"Zhao","suffix":""},{"id":592816269,"identity":"3e241cf6-0ce0-49e1-87d2-12efd714612d","order_by":3,"name":"Danyang Jia","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Danyang","middleName":"","lastName":"Jia","suffix":""},{"id":592816270,"identity":"a246bde0-e0c2-478b-b50d-b5f2e72c7b1f","order_by":4,"name":"Zhen Wang","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-02-05 03:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8791539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8791539/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103507473,"identity":"65b7bed7-8dad-402c-8770-c617ddcfbf28","added_by":"auto","created_at":"2026-02-26 13:41:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":550999,"visible":true,"origin":"","legend":"","description":"","filename":"HowAuthorNetworkCentralityDrivesCitationDisparitiesv1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8791539/v1_covered_36426f73-5ff3-47ec-bab9-447d51d59a0b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eBeyond Content: How Author Network Centrality Drives Citation Disparities in Top AI Conferences\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8791539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8791539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study investigates how authors' structural positions within scientific collaboration networks drive citation disparities beyond research content. We analyze 17,942 papers from three top AI conferences (NeurIPS, ICLR and ICML) spanning 2005 to 2024. To address the limitations of existing metrics, we introduce a novel centrality measure, HCTCD, which incorporates temporal decay and collaboration intensity. To isolate the effect of network structure, we rigorously control for textual content via a pre-trained transformer encoder. The relationship is then modeled using Beta regression, with the citation percentile within each publication year as the dependent variable. Our results demonstrate that long-term centrality exerts a stronger influence than short-term metrics. Critically, team-level centrality aggregation outperforms author-rank approaches. Furthermore, after controlling for content and other variables, incorporating a co-author with centrality 50\\% higher than the first author is associated with a significant increase in expected citations, illustrating a clear “social lift”. 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