Detecting Fuzzy Community Structures in Complex Networks with a Potts Model
Jörg ReichardtInterdisciplinary Center for Bioinformatics, University of Leipzig, Kreuzstrasse 7b, D-04103 Leipzig, GermanyStefan BornholdtInterdisciplinary Center for Bioinformatics, University of Leipzig, Kreuzstrasse 7b, D-04103 Leipzig, Germany
2004en
ABI
Abstract
A fast community detection algorithm based on a q-state Potts model is presented. Communities (groups of densely interconnected nodes that are only loosely connected to the rest of the network) are found to coincide with the domains of equal spin value in the minima of a modified Potts spin glass Hamiltonian. Comparing global and local minima of the Hamiltonian allows for the detection of overlapping ("fuzzy") communities and quantifying the association of nodes with multiple communities as well as the robustness of a community. No prior knowledge of the number of communities has to be assumed.
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