A fast multilevel algorithm for graph clustering and community detection
msra(2007)
摘要
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.
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关键词
data analysis,complete graph,graph partitioning,minimum cut,graph clustering
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