Finding community structure in very large networks Aaron Clauset,1 M. E. J. Newman,2 and Cristopher Moore1, 3 1 Department of Computer Science, University of New Mexico, Albuquerque, NM 87131 2 Department of Physics and Center for the Study of Complex...
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Finding community structure in very large networks Aaron Clauset,1 M. E. J. Newman,2 and Cristopher Moore1, 3 1 Department of Computer Science, University of New Mexico, Albuquerque, NM 87131 2 Department of Physics and Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109 3 Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM 87131 The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(md log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m ∼ n and d ∼ log n,
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