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      Fast unfolding of communities in large networks

      Author(s): , , ,
      Publication date Created:
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      Journal: Journal of Statistical Mechanics: Theory and Experiment
      Publisher: IOP Publishing

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          Abstract

          <p class="first" id="d25859e56">Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008</p>

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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.
          Article 0 comments Cited 1003 times – based on 0 reviews     Review now
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            Statistical mechanics of complex networks

            Réka Albert, Albert-László Barabási (2002)
            Reviews of Modern Physics, 74(1), 47-97
            Article 0 comments Cited 740 times – based on 0 reviews     Review now
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              Community structure in social and biological networks.

              M Girvan, M. E. J. Newman (2002)
              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.
              Article 0 comments Cited 735 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Title: Journal of Statistical Mechanics: Theory and Experiment
                Abbreviated Title: J. Stat. Mech.
                Publisher: IOP Publishing
                ISSN (Electronic): 1742-5468
                Publication date Created: October 01 2008
                Publication date (Electronic): October 09 2008
                Volume: 2008
                Issue: 10
                Page: P10008
                Article
                DOI: 10.1088/1742-5468/2008/10/P10008
                SO-VID: 58180faf-c6c2-4670-82eb-e5f042c5ecc1
                Copyright © © 2008
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