Abstract
In network, nodes are joined together in tightly knit groups. Local group information is used to search the natural community. It can be crucial to help us to understand the functional properties of the networks and detect the true community structure. In this paper, we propose an algorithm called Three-way Decision Community Detection Algorithm based on Local Group Information(LGI-TWD) to detect community structure by using local group information. Firstly, we define sub-communities of each node v. Node v and v’s neighbors which are reachable to each other construct one sub-communities of node v. Then, each sub-communities is regarded as a granular, and then hierarchical structure is constructed based on granulation coefficient. Finally, a further classification for boundary region’s nodes can be done according to belonging degree. Compared with other community detection algorithms (N-TWD, CACDA, GN, NFA, LPA), the experimental results on six real world social networks show that LGI-TWD gets higher modularity value Q and more accurate communities.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Nos. 61602003, 61673020, 61402006), supported by the Provincial Natural Science Foundation of Anhui Province (Nos. 1508085MF113, 1708085QF156, 1708085QF143) supported by Provincial Natural Science Research Program of Higher Education Institutions of Anhui Province (Nos. KJ2013A016, KJ2016A016), supported by MOE (Ministry of Education in China) Project of Humanities, supported by Social Sciences (No.14YJC860020) and supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (49th).
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Chen, J. et al. (2017). Three-Way Dicision Community Detection Algorithm Based on Local Group Information. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10314. Springer, Cham. https://doi.org/10.1007/978-3-319-60840-2_12
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DOI: https://doi.org/10.1007/978-3-319-60840-2_12
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