Detecting fuzzy community structures in complex networks with a q-state Potts model
Abstract
We introduce a new community detection algorithm based on a q-state Potts model that allows for fuzzy communities. Communities are found as domains of equal spin value in the ground state of a modified Hamiltonian. No prior knowledge of the number of communities has to be assumed. Measuring affiliation ratios of nodes to communities can account for overlapping communities and quantify the association strength of nodes to communities as well as the stability of a community. It performs remarkably well on both computer generated as well as real world networks, outperforming commonly used bi-partitioning algorithms.Keywords
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