Multi-resolution community detection based on generalized self-loop rescaling strategy

Ju Xiang, Yan-Ni Tang, Yuan-Yuan Gao, Yan Zhang, Ke Deng, Xiao-Ke Xu and Ke Hu

Physica A: Statistical Mechanics and its Applications, 2015, vol. 432, issue C, 127-139

Abstract: Community detection is of considerable importance for analyzing the structure and function of complex networks. Many real-world networks may possess community structures at multiple scales, and recently, various multi-resolution methods were proposed to identify the community structures at different scales. In this paper, we present a type of multi-resolution methods by using the generalized self-loop rescaling strategy. The self-loop rescaling strategy provides one uniform ansatz for the design of multi-resolution community detection methods. Many quality functions for community detection can be unified in the framework of the self-loop rescaling. The resulting multi-resolution quality functions can be optimized directly using the existing modularity-optimization algorithms. Several derived multi-resolution methods are applied to the analysis of community structures in several synthetic and real-world networks. The results show that these methods can find the pre-defined substructures in synthetic networks and real splits observed in real-world networks. Finally, we give a discussion on the methods themselves and their relationship. We hope that the study in the paper can be helpful for the understanding of the multi-resolution methods and provide useful insight into designing new community detection methods.

Keywords: Complex networks; Community detection; Multi-resolution methods (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:432:y:2015:i:c:p:127-139

DOI: 10.1016/j.physa.2015.03.006

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