A Novel Ranking Scheme for Identifying Influential Nodes in Complex Networks

A Novel Ranking Scheme for Identifying Influential Nodes in Complex Networks | 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 A Novel Ranking Scheme for Identifying Influential Nodes in Complex Networks Qing Yang, Jiafei Liu, Dajin Wang, Jingli Wu, Gaoshi Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7734731/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 31 You are reading this latest preprint version Abstract Identifying influential nodes in complex networks, such as social networks, is crucial in many applications including information dissemination, virus protection , community detection, etc., and many methods have been proposed for identifying influential nodes. Most existing methods only consider one single attribute of nodes, which for some applications may not be sufficient, and can lead to low resolution among nodes. In this paper, we propose a novel method for identifying influential nodes−CI-based gravity model (CIGM)−which integrates the integrated degree and I-shell to evaluate the impact of nodes in an all-around manner. It aims at offering a more comprehensive measure of the node’s influence than the known methods. We conducted comparison experiments for CIGM against ten baseline methods using twelve real-world datasets, in terms of the SIR model, the Kendall’s correlation coefficient, CCDF and monotonicity index. The experimental results on twelve real-world networks show that CIGM not only identifies influential nodes effectively but also captures nodes of strategic importance, those that help enhance performance in information dissemination and network connectivity, suggesting that CIGM holds advantageousness and broader applicability over the existing schemes. Complex networks Influential nodes Integrated degree I-shell Susceptible-Infected-Recovered model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Oct, 2025 Reviews received at journal 23 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviews received at journal 19 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviews received at journal 11 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor assigned by journal 09 Oct, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 28 Sep, 2025 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. 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