Sciweavers

Share
ISCIS
2005
Springer

Computing Communities in Large Networks Using Random Walks

8 years 10 months ago
Computing Communities in Large Networks Using Random Walks
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(mn2 ) and space O(n2 ) in the worst case, and in time O(n2 log 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). 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.
Pascal Pons, Matthieu Latapy
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ISCIS
Authors Pascal Pons, Matthieu Latapy
Comments (0)
books