Measuring node dissimilarity and seeking communities in directed networks via signal copying

Bo Yang, Tao Huang, Xu Li, Zhiyong Pei, Weizheng Cheng and Xuelin Huang
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Bo Yang: School of Automation, Wuhan University of Technology, Wuhan 430070, P. R. China
Tao Huang: School of Automation, Wuhan University of Technology, Wuhan 430070, P. R. China
Xu Li: School of Automation, Wuhan University of Technology, Wuhan 430070, P. R. China
Zhiyong Pei: #x2020;Green & Smart River-Sea-Going Ship, Cruise and Yacht Research Center, Wuhan University of Technology, Wuhan 430063, P. R. China
Weizheng Cheng: School of Automation, Wuhan University of Technology, Wuhan 430070, P. R. China
Xuelin Huang: School of Automation, Wuhan University of Technology, Wuhan 430070, P. R. China

International Journal of Modern Physics C (IJMPC), 2021, vol. 32, issue 10, 1-17

Abstract: We consider the problem of seeking communities in directed networks. The common approach to this problem has been to simply ignore directions of edges and use algorithms developed for community detection in undirected networks, but this approach discards potentially indispensable information involved in the directions of edges. Here, we introduce an index for node dissimilarity in directed networks in the perspective of dynamics, which are driven by a novel parameter-free signal copying model, to incorporate information contained in the directions of edges. Our dissimilarity index gives a natural fusion between the local and global viewpoints of topological structure of directed networks. Thereby, we apply our index to uncover community structure in directed networks and conduct extensive experiments on synthetic and real-world ones. The results demonstrate that our method outperforms state-of-the-art methods.

Keywords: Directed networks; signal copying; node dissimilarity; community detection (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0129183121501369

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