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Scalable detection of statistically significant communities and hierarchies, using message passing for modularity
2014
Proceedings of the National Academy of Sciences of the United States of America
Modularity is a popular measure of community structure. However, maximizing the modularity can lead to many competing partitions, with almost the same modularity, that are poorly correlated with each other. It can also produce illusory "communities" in random graphs where none exist. We address this problem by using the modularity as a Hamiltonian at finite temperature, and using an efficient Belief Propagation algorithm to obtain the consensus of many partitions with high modularity, rather
doi:10.1073/pnas.1409770111
pmid:25489096
pmcid:PMC4280643
fatcat:5bc4kci2s5gxxbmduarf2luqyy