Detecting communities from networks based on their intrinsic properties

Haijuan Yang, Jianjun Cheng, Mingwei Leng (), Xing Su (), Wenbo Zhang () and Xiaoyun Chen
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Haijuan Yang: School of Information Science and Engineering, Lanzhou University, No. 222, Tianshui South Road, Lanzhou 730000, P. R. China†Department of Electronic Information Engineering, Lanzhou Vocational Technical College, No. 37, Liusha Road, Lanzhou 730070, P. R. China
Jianjun Cheng: School of Information Science and Engineering, Lanzhou University, No. 222, Tianshui South Road, Lanzhou 730000, P. R. China
Mingwei Leng: #x2021;School of Education Science and Technology, Northwest Minzu University, No. 1, Xibei Xincun, Lanzhou 730030, P. R. China
Xing Su: School of Information Science and Engineering, Lanzhou University, No. 222, Tianshui South Road, Lanzhou 730000, P. R. China
Wenbo Zhang: 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 12, 1-24

Abstract: Communities in networks expose some intrinsic properties, each of them involves some influential nodes as its cores, around which the entire community grows gradually; the more the common neighbors that exist between a pair of nodes, the larger the possibility of belonging to the same community; the more the neighbors of any one node belong to a community, the larger the possibility that node belongs to that community too. In this paper, we present a novel method, which makes full utilization of these intrinsic properties to detect communities from networks. We iteratively select the node with the largest degree from the remainder of the network as the first seed of a community, then consider its first- and second-order neighbors to identify other seeds of the community, then expand the community by attracting nodes whose large proportion of neighbors have been in the community to join. In this way, we obtain a series of communities. However, some of them might be too small to make sense. Therefore, we merge some of the initial communities into larger ones to acquire the final community structure. In the entire procedure, we try to keep nodes in every community to be consistent with the properties as possible as we can, this leads to a high-quality result. Moreover, the proposed method works with a higher efficiency, it does not need any prior knowledge about communities (such as the number or the size of communities), and does not need to optimize any objective function either. We carry out extensive experiments on both some artificial networks and some real-world networks to testify the proposed method, the experimental results demonstrate that both the efficiency and the community-structure quality of the proposed method are promising, our method outperforms the competitors significantly.

Keywords: Community detection; intrinsic properties; common neighbors; community expansion (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0129183119501043

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