An improved algorithm for detecting community defined by node-to-node dynamic distance

Jiaxin Wan, Dingding Han, Zhengzhuang Yang and Ming Tang
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Jiaxin Wan: School of Information Science and Technology, Fudan University, Shanghai 200433, P. R. China†School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, P. R. China
Dingding Han: School of Information Science and Technology, Fudan University, Shanghai 200433, P. R. China
Zhengzhuang Yang: School of Information Science and Technology, Fudan University, Shanghai 200433, P. R. China
Ming Tang: #x2021;School of Physics and Electronic Science, East China Normal University, Shanghai 200241, P. R. China

International Journal of Modern Physics C (IJMPC), 2020, vol. 31, issue 11, 1-18

Abstract: The study of community structure is of great significance when analyzing the structural and functional characteristics of networks. Attractor is a fast community detection method with the advantage of high accuracy for complex networks. However, in the connected nodes interaction model proposed by the Attractor algorithm, there is a problem with slow convergence during the distance updating process. To solve this problem, we propose an improved Attractor algorithm based on the change trend of the distances between connected nodes. We have generally found that distances between connected nodes exhibit a consistent trend. The dynamic distance trend is determined by setting a window of evaluation. The convergence of the Attractor algorithm is accelerated by the consistent change trend. Experiments on datasets for real-world networks and synthetic networks have shown that our proposed algorithm not only maintains high-quality communities, but also reduces the calculation time significantly and greatly improves the speed of the algorithm.

Keywords: Community detection; improved attractor algorithm; node-to-node dynamic distance (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0129183120501557

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