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Finding community structure in very large networks

Clauset, Aaron ; Newman, M E J ; Moore, Cristopher

Physical review. E, Statistical, nonlinear, and soft matter physics, 2004-12, Vol.70 (6 Pt 2), p.066111-066111, Article 066111 [Periódico revisado por pares]

United States

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  • Título:
    Finding community structure in very large networks
  • Autor: Clauset, Aaron ; Newman, M E J ; Moore, Cristopher
  • É parte de: Physical review. E, Statistical, nonlinear, and soft matter physics, 2004-12, Vol.70 (6 Pt 2), p.066111-066111, Article 066111
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: 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 approximately n and d approximately log n, in which case our algorithm runs in essentially linear time, O (n log(2) n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2 x 10(6) edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.
  • Editor: United States
  • Idioma: Inglês
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