Computing Communities in Large Networks Using Random Walks

  • Pascal Pons
  • Matthieu Latapy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3733)

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, 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pascal Pons
    • 1
  • Matthieu Latapy
    • 1
  1. 1.LIAFAUniversité Paris Denis Diderot and CNRSParisFrance

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