Journal article

Modularity and Community Structure in Networks

Newman M ...see all

Source: Proceedings of the National Academy of Sciences of the United States of America, vol. 103, issue 23 (2006) pp. 8577-8582

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Abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Many networks of interest in the sciences, including social net-works, computer networks, and metabolic and regulatory net-works, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community struc-ture 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 net-work, 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. clusteringI partitioning I modulesI metabolic network social network

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Authors

  • M E J Newman

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