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      Fast unfolding of communities in large networks

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          Abstract

          Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008

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          Most cited references28

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as "modularity" over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.
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            Community structure in social and biological networks.

            A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.
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              Statistical mechanics of complex networks

              Reviews of Modern Physics, 74(1), 47-97
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                Author and article information

                Journal
                IOP Publishing
                2008
                09 October 2008
                18 June 2017
                Article
                10.1088/1742-5468/2008/10/P10008
                21517554
                58180faf-c6c2-4670-82eb-e5f042c5ecc1
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