@article{Rosvall29012008,
  abstract = {To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network—including physics, chemistry, molecular biology, and medicine—information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.},
  author = {Rosvall, Martin and Bergstrom, Carl T.},
  doi = {10.1073/pnas.0706851105},
  eprint = {http://www.pnas.org/content/105/4/1118.full.pdf+html},
  interhash = {8192f8db9fce0417034311e81a477838},
  intrahash = {ffe2c7ca3a20430f60dfd138e72df5f5},
  journal = {Proceedings of the National Academy of Sciences},
  number = 4,
  pages = {1118-1123},
  title = {Maps of random walks on complex networks reveal community structure},
  url = {http://www.pnas.org/content/105/4/1118.abstract},
  volume = 105,
  year = 2008
}

@article{PhysRevLett.100.118703,
  author = {Leicht, E. A. and Newman, M. E. J.},
  doi = {10.1103/PhysRevLett.100.118703},
  interhash = {825411a28bde71cda1c9087fc329d963},
  intrahash = {93726cc0540f75ee1cb515b2923d69e8},
  journal = {Phys. Rev. Lett.},
  month = mar,
  number = 11,
  numpages = {4},
  pages = 118703,
  publisher = {American Physical Society},
  title = {Community Structure in Directed Networks},
  url = {http://prl.aps.org/abstract/PRL/v100/i11/e118703},
  volume = 100,
  year = 2008
}

@article{clauset-2004-70,
  author = {Clauset, Aaron and Newman, M.E.J. and Moore, Cristopher},
  interhash = {2c68e3c981a00380692a3b0b661d7cfd},
  intrahash = {a35d69f1d41a6cdd0632c5e1cadb4d44},
  journal = {Physical Review E},
  pages = 066111,
  title = {Finding community structure in very large networks},
  url = {http://www.citebase.org/cgi-bin/citations?id=oai:arXiv.org:cond-mat/0408187},
  volume = 70,
  year = 2004
}

@article{newman2004finding,
  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 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. },
  author = {Newman, M.E.J. and Girvan, M.},
  doi = {10.1103/PhysRevE.69.026113},
  interhash = {b9145040e35ccb4d2a0ce18105e64ff4},
  intrahash = {5581d4204604967a209dcc712ac391af},
  journal = {Physical Review E},
  pages = 026113,
  title = {Finding and evaluating community structure in networks},
  url = {http://arxiv.org/abs/cond-mat/0308217},
  volume = 69,
  year = 2004
}

@inbook{tyler2003email,
  abstract = {We describe a method for the automatic identification of communities of practice from email logs within an organization. We use a betweenness centrality algorithm that can rapidly find communities within a graph representing information flows. We apply this algorithm to an email corpus of nearly one million messages collected over a two-month span, and show that the method is effective at identifying true communities, both formal and informal, within these scale-free graphs. This approach also enables the identification of leadership roles within the communities. These studies are complemented by a qualitative evaluation of the results in the field.},
  address = {Deventer, The Netherlands, The Netherlands},
  author = {Tyler, Joshua R. and Wilkinson, Dennis M. and Huberman, Bernardo A.},
  booktitle = {Communities and technologies},
  interhash = {c712e59ff99f12c42a5d3c3b0bf4c48f},
  intrahash = {b272b4797aec6d5e3a4972592af93ab2},
  pages = {81--96},
  publisher = {Kluwer, B.V.},
  title = {Email as Spectroscopy: Automated Discovery of Community Structure within Organizations},
  url = {http://www.citebase.org/cgi-bin/citations?id=oai:arXiv.org:cond-mat/0303264},
  year = 2003
}

@article{gn02community,
  abstract = {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.},
  author = {Girvan, Michelle and Newman, M.E.J.},
  interhash = {ecd7a48a37f660ab421472140168c892},
  intrahash = {8f80a8586927ea69ea915b6c32e87629},
  journal = {Proceedings of the National Academy of Science},
  number = 12,
  pages = {7821-7826},
  title = {Community structure in social and biological networks},
  volume = 99,
  year = 2002
}