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      Finding and evaluating community structure in networks.

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          Abstract

          We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.

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          Author and article information

          Journal
          Phys Rev E Stat Nonlin Soft Matter Phys
          Physical review. E, Statistical, nonlinear, and soft matter physics
          American Physical Society (APS)
          1539-3755
          1539-3755
          Feb 2004
          : 69
          : 2 Pt 2
          Affiliations
          [1 ] Department of Physics and Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109-1120, USA.
          Article
          10.1103/PhysRevE.69.026113
          14995526
          8d52b3a1-89c7-41d9-8ded-c1ccb632b659
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