Detecting fuzzy community structures in complex networks with a Potts model
Statistical Mechanics
2009-11-10 v2 Disordered Systems and Neural Networks
Abstract
A fast community detection algorithm based on a q-state Potts model is presented. Communities in networks (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 to multiple communities as well as the robustness of a community. No prior knowledge of the number of communities has to be assumed.
Cite
@article{arxiv.cond-mat/0402349,
title = {Detecting fuzzy community structures in complex networks with a Potts model},
author = {Joerg Reichardt and Stefan Bornholdt},
journal= {arXiv preprint arXiv:cond-mat/0402349},
year = {2009}
}
Comments
Replacement with conceptual changes, inclusion of benchmarks and large real world applications. 4 pages, 4 figures