Computing communities in large networks using random walks

Authors:
Pascal Pons;Matthieu Latapy
Affiliations:
LIAFA, Université Paris Denis Diderot and CNRS, Paris, France;LIAFA, Université Paris Denis Diderot and CNRS, Paris, France
Venue:
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Year:
2005

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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).