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Scalable and Accurate Algorithm for Graph Clustering ?

One of the most useful measures of quality for graph clustering is the modularity of the partition, which measures the difference between the number of the edges with endpoints in the same cluster and the expected number of such edges in a random 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 resulting minimum cut problem can be efficiently solved by adapting existing graph partitioning tools. Our algorithm is accurate and finds a graph clusterings much faster than alternative algorithms that produce a comparable clustering quality.