A fast multilevel algorithm for graph clustering and community detection

Abstract

One of the most useful measures of cluster quality is the modularity of a partition, which measures the difference between the number of the edges joining vertices from the same cluster and the expected number of such edges in a random (unstructured) graph. In this paper we show that the problem of finding a partition maximizing the modularity of a given graph G can be reduced to a minimum weighted cut problem on a complete graph with the same vertices as G. We then show that the resulted minimum cut problem can be efficiently solved with existing software for graph partitioning and that our algorithm finds clusterings of a better quality and much faster than the existing clustering algorithms.

12 pages, 2 figures; Workshop on Algorithms and Models for the Web Graph, 2006

References in corpus (8)

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• Community structure in social and biological networks
• Finding and evaluating community structure in networks
• Finding community structure in very large networks
• Fast algorithm for detecting community structure in networks
• Finding community structure in networks using the eigenvectors of matrices
• Cooperative Game Theory Approaches for Network Partitioning
• Mixing patterns in networks