Degree Centrality-Based Influence Maximization by Community Detection and Finding Bridging Nodes

Degree Centrality-Based Influence Maximization by Community Detection and Finding Bridging Nodes | 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 Degree Centrality-Based Influence Maximization by Community Detection and Finding Bridging Nodes Himansu Sekhar Pattanayak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6553321/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 Degree centrality is one of the simplest centrality metrics used to rank the nodes of a network, which in-turn helps in selecting the most influential nodes for influence maximization. The centrality measures such as betweenness centrality measure have better spreading with higher computational complexity. Whereas, centrality measures such as degree have lower computational complexity with inferior spreading. In this work, we a have partitioned the network to create communities (clusters) of nodes, and formed abstract network with communities as super nodes. We have ranked the super nodes based on their degree centrality, with respect to the abstract network. The total seed nodes are distributed across the the communities. The community with greater degree centrality is given preference for allocation of seed nodes. Inside each community, higher preference is given to the nodes with higher degree and better connectivity to nodes outside its own community . In-terms of computational complexity, the proposed method is similar to degree centrality; whereas, in-terms of spreading, its is either comparable or supersedes betweenness centrality for few networks. 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. 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