Detecting modules in dense weighted networks with the Potts method

Abstract

We address the problem of multiresolution module detection in dense weighted networks, where the modular structure is encoded in the weights rather than topology. We discuss a weighted version of the q-state Potts method, which was originally introduced by Reichardt and Bornholdt. This weighted method can be directly applied to dense networks. We discuss the dependence of the resolution of the method on its tuning parameter and network properties, using sparse and dense weighted networks with built-in modules as example cases. Finally, we apply the method to data on stock price correlations, and show that the resulting modules correspond well to known structural properties of this correlation network.

14 pages, 6 figures. v2: 1 figure added, 1 reference added, minor changes. v3: 3 references added, minor changes

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Cited by in corpus (10)

• Community detection in graphs
• Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications
• Multiresolution community detection for megascale networks by information-based replica correlations
• Local resolution-limit-free Potts model for community detection
• Community detection for correlation matrices
• Multiscale core-periphery structure in a global liner shipping network
• Hierarchical mutual information for the comparison of hierarchical community structures in complex networks
• Synchronization over $Z_2$ and community detection in multiplex signed networks with constraints
• Enhancing community detection by local structural information
• Detectability threshold in weighted modular networks