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• W2152091390 abstract "Measuring the centrality of nodes in real-world networks has remained an important task in the technological, social, and biological network paradigms carrying implications on their analysis and applications. Exact inference of centrality values is infeasible in large networks due to the need to solve the all-pairs shortest path problem. We introduce a framework to approximate node centralities in real-world networks that are known to exhibit modularity, i.e., the presence of dense subgraphs or communities, which are themselves sparsely connected. We also propose a novel centrality measure known as Community Inbetweenness that ranks nodes based solely on community information. In a modular network of size n with √n(1/2) evenly sized communities and m edges, our framework requires linear time O(m) and O(√nm) time for the approximation of closeness and betweenness respectively. Utilizing a recently proposed linear time method in community detection, our approximation techniques are faster than traditional sampling algorithms, applicable in real-time distributed environments, and offer highly comparable results." @default.
• W2152091390 created "2016-06-24" @default.
• W2152091390 creator A5050965411 @default.
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• W2152091390 date "2009-11-06" @default.
• W2152091390 modified "2023-09-27" @default.
• W2152091390 title "Fast centrality approximation in modular networks" @default.
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• W2152091390 doi "https://doi.org/10.1145/1651274.1651282" @default.
• W2152091390 hasPublicationYear "2009" @default.
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