We propose and study a set of algorithms for discovering community structure in networksnatural 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.
REF proposed a method for discovering communities based on hierarchical divisive algorithms where an edge is removed iteratively from the network to split it into communities.
Finding and evaluating community structure in networks
Phys. Rev. E 69, 026113 (2004)
Physics Medicine