@article{newman2004finding,
  author = {Newman, M. E. J. and Girvan, M.},
  doi = {10.1103/PhysRevE.69.026113},
  interhash = {b9145040e35ccb4d2a0ce18105e64ff4},
  intrahash = {1dbc30a1818aa74973f387162e485443},
  journal = {Phys. Rev. E},
  month = feb,
  number = 2,
  numpages = {15},
  pages = 026113,
  publisher = {American Physical Society},
  title = {Finding and evaluating community structure in networks},
  url = {http://link.aps.org/doi/10.1103/PhysRevE.69.026113},
  volume = 69,
  year = 2004
}

@article{NewGir04,
  author = {Newman, M. E. J. and Girvan, M.},
  interhash = {b9145040e35ccb4d2a0ce18105e64ff4},
  intrahash = {63176454110326cb664a25fc249c8f7b},
  journal = {Physical Review},
  number = 026113,
  title = {Finding and evaluating community structure in networks},
  volume = {E 69},
  year = 2004
}

@article{girvan2002,
  author = {Girvan, M. and Newman, M. E. J.},
  file = {full paper:Girvan2002.pdf:PDF},
  groups = {public},
  interhash = {ecd7a48a37f660ab421472140168c892},
  intrahash = {da908a587645fb450af65cbbb63323a7},
  journal = {Proceedings of the National Academy of Sciences},
  number = 12,
  pages = {7821-7826},
  title = {Community structure in social and biological networks},
  username = {lantiq},
  volume = 99,
  year = 2002
}

@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 any 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 and Girvan, M},
  interhash = {b9145040e35ccb4d2a0ce18105e64ff4},
  intrahash = {0c522f0a01f72638e70916f1144746e6},
  journal = {Phys Rev E Stat Nonlin Soft Matter Phys},
  month = Feb,
  number = 2,
  pages = {026113.1-15},
  pmid = {14995526},
  title = {Finding and evaluating community structure in networks},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/14995526},
  volume = 69,
  year = 2004
}

@article{Newman04communityStructure,
  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 any 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 and Girvan, M},
  interhash = {b9145040e35ccb4d2a0ce18105e64ff4},
  intrahash = {0c522f0a01f72638e70916f1144746e6},
  journal = {Phys Rev E Stat Nonlin Soft Matter Phys},
  month = feb,
  number = 2,
  pages = {026113.1-15},
  pmid = {14995526},
  title = {Finding and evaluating community structure in networks},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/14995526},
  volume = 69,
  year = 2004
}

@article{GirNew02,
  author = {Girvan, M. and Newman, M. E. J.},
  interhash = {ecd7a48a37f660ab421472140168c892},
  intrahash = {ec20851eb4909dd27cefec2dc9883fa4},
  journal = {PNAS},
  month = {June},
  number = 12,
  pages = {7821-7826},
  title = {Community structure in social and biological networks},
  volume = 99,
  year = 2002
}

@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
}