Limited resolution in complex network community detection with Potts model approach

Abstract

According to Fortunato and Barthelemy, modularity-based community detection algorithms have a resolution threshold such that small communities in a large network are invisible. Here we generalize their work and show that the q-state Potts community detection method introduced by Reichardt and Bornholdt also has a resolution threshold. The model contains a parameter by which this threshold can be tuned, but no a priori principle is known to select the proper value. Single global optimization criteria do not seem capable for detecting all communities if their size distribution is broad.

5 pages, 3 figures, final version

References in corpus (6)

• Uncovering the overlapping community structure of complex networks in nature and society
• Finding community structure in networks using the eigenvectors of matrices
• Resolution limit in community detection
• Comparing community structure identification
• Statistical Mechanics of Community Detection
• Preferential attachment of communities: the same principle, but a higher level

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