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• W1659336119 abstract "Dense subgraphs of sparse graphs (communities), which appear in most real-world complex networks, play an important role in many contexts. Computing them however is generally expensive. We propose here a measure of similarities between vertices based on random walks which has several important advantages: it captures well the community structure in a network, it can be computed efficiently, and it can be used in an agglomerative algorithm to compute efficiently the community structure of a network. We propose such an algorithm, called Walktrap, which runs in time O(mn^2) and space O(n^2) in the worst case, and in time O(n^2log n) and space O(n^2) in most real-world cases (n and m are respectively the number of vertices and edges in the input graph). Extensive comparison tests show that our algorithm surpasses previously proposed ones concerning the quality of the obtained community structures and that it stands among the best ones concerning the running time." @default.
• W1659336119 created "2016-06-24" @default.
• W1659336119 creator A5031952531 @default.
• W1659336119 creator A5077965394 @default.
• W1659336119 date "2005-12-12" @default.
• W1659336119 modified "2023-09-27" @default.
• W1659336119 title "Computing communities in large networks using random walks (long version)" @default.
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