@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
}