Detecting communities from networks using an improved self-organizing map

Jianjun Cheng, Shiyan Zhao (), Haijuan Yang (), Jingming Zhang (), Xing Su () and Xiaoyun Chen
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Jianjun Cheng: School of Information Science and Engineering, Lanzhou University, No. 222, Tianshui South Road, Lanzhou 730000, P. R. China
Shiyan Zhao: School of Information Science and Engineering, Lanzhou University, No. 222, Tianshui South Road, Lanzhou 730000, P. R. China
Haijuan Yang: #x2020;Department of Electronic Information Engineering, Lanzhou Vocational Technical College, No. 37, Liusha Road, Lanzhou 730070, P. R. China
Jingming Zhang: School of Information Science and Engineering, Lanzhou University, No. 222, Tianshui South Road, Lanzhou 730000, P. R. China
Xing Su: School of Information Science and Engineering, Lanzhou University, No. 222, Tianshui South Road, Lanzhou 730000, P. R. China
Xiaoyun Chen: School of Information Science and Engineering, Lanzhou University, No. 222, Tianshui South Road, Lanzhou 730000, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 06, 1-22

Abstract: Community structure is one of the important features of complex networks. Researchers have derived a number of algorithms for detecting communities, some of them suffer from high complexity or need some prior knowledge, such as the size of community or number of communities. For some of them, the quality of the detected community structure cannot be guaranteed, even the results of some of them are nondeterministic. In this paper, we propose a Self-Organizing Map (SOM)-based method for detecting community structure from networks. We first preprocess the network by removing some nodes and their associated edges which have little contribution to the formation of communities, then we construct the extended attribute matrix from the preprocessed network, next we embed the detecting procedure in the training of SOM on the attribute matrix to acquire the initial community structure, and finally, we handle those removed nodes by inserting each of them into the community to which its only neighbor belongs, and fine-tune the initial community structure by merging some of the initial communities to improve the quality of the final result. The performance of the proposed method is evaluated on a variety of artificial networks and real-world networks, and experimental results show that our method takes full advantage of SOM model, it can automatically determine the number of communities embedded in the network, the quality of the detected community structure is steadily promising and superior to those of other comparison algorithms.

Keywords: Community detection; self-organizing map; Training (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0129183119500542

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