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

References in corpus (15)

• Uncovering the overlapping community structure of complex networks in nature and society
• Resolution limit in community detection
• Statistical Mechanics of Community Detection
• Structure and tie strengths in mobile communication networks
• Prediction and predictability of global epidemics: the role of the airline transportation network
• Analysis of the structure of complex networks at different resolution levels
• A Bayesian Approach to Network Modularity
• Weighted network modules
• Emergence of communities in weighted networks
• Community Detection as an Inference Problem
• Limited resolution in complex network community detection with Potts model approach
• Sector analysis for a FTSE portfolio of stocks
• Spectrum of genetic diversity and networks of clonal organisms
• Spectral and network methods in the analysis of correlation matrices of stock returns
• Spectral methods and cluster structure in correlation-based networks