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      Reconstruction and estimation in the planted partition model

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      Probability Theory and Related Fields
      Springer Nature

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          Normalized cuts and image segmentation

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            Is Open Access

            Finding and evaluating community structure in networks

            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 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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              Hierarchical clustering schemes.

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                Author and article information

                Journal
                Probability Theory and Related Fields
                Probab. Theory Relat. Fields
                Springer Nature
                0178-8051
                1432-2064
                August 2015
                July 2014
                : 162
                : 3-4
                : 431-461
                Article
                10.1007/s00440-014-0576-6
                3ca42183-4bac-49e0-b375-0ee46fb43109
                © 2015
                History

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