Computing Communities in Large Networks Using Random Walks
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- Pons P., Latapy M. (2005) Computing Communities in Large Networks Using Random Walks. In: Yolum ., Güngör T., Gürgen F., Özturan C. (eds) Computer and Information Sciences - ISCIS 2005. ISCIS 2005. Lecture Notes in Computer Science, vol 3733. Springer, Berlin, Heidelberg
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, it works at various scales, and it can be used in an agglomerative algorithm to compute efficiently the community structure of a network. We propose such an algorithm which runs in time O(mn2) and space O(n2) in the worst case, and in time O(n2log n) and space O(n2) in most real-world cases (n and m are respectively the number of vertices and edges in the input graph).
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