Community detection via closure extension

Jingming Zhang, Jianjun Cheng (), Xiaosu Feng () and Xiaoyun Chen ()
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Jingming Zhang: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Jianjun Cheng: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Xiaosu Feng: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Xiaoyun Chen: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China

International Journal of Modern Physics C (IJMPC), 2018, vol. 29, issue 12, 1-17

Abstract: Identifying community structure in networks plays an important role in understanding the network structure and analyzing the network features. Many state-of-the-art algorithms have been proposed to identify the community structure in networks. In this paper, we propose a novel method based on closure extension; it performs in two steps. The first step uses the similarity closure or correlation closure to find the initial community structure. In the second step, we merge the initial communities using Modularity Q. The proposed method does not need any prior information such as the number or sizes of communities, and it is able to obtain the same resulting communities in multiple runs. Moreover, it is noteworthy that our method has low computational complexity because of considering only local information of network. Some real-world and synthetic graphs are used to test the performance of the proposed method. The results demonstrate that our method can detect deterministic and informative community structure in most cases.

Keywords: Complex networks; community detection; closure extension; modularity (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S012918311850119X

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