Affiliations: [a] School of Basic Courses, Bengbu Medical College, Bengbu 233000, Anhui China | [b] School of Mathematics and Computer Science, Anhui Normal University, Wuhu 241000, Anhui, China
Correspondence:
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Corresponding author: Yang Wang, School of Mathematics and Computer Science, Anhui Normal University, Wuhu 241000, Anhui, China. E-mail: [email protected].
Abstract: With the rapid development of social network, community detection has caught lots of researcher’s attention. However, the existing methods are difficult to solve mass data in growing large-scale networks. In this paper, a two-step method is proposed to cope with large-scale networks based on the construction of community tree and the N-players cooperative game process. In the first stage, the community tree is constructed to initialize community detection by the similarity of nodes. Next, it performs greatly for N-players cooperative game to adjust and ensure the remaining nodes or spare communities. For the transmission of the stored structure of community tree, N-players cooperative game theory (NC-GT) also exhibits the excellent performance on runtime. We make the evaluations on three synthetic networks and four real-world networks and the main indicates are the modularity, NMI etc. The results show NC-GT has a stable and efficient performance on large-scale compared to other algorithms. Besides, the proposed community tree provides a more convenient way to research the flexible actions on nodes, which makes it more suitable for the dynamic networks.
Keywords: Large-scale social networks, multi-stage, community tree, N-players cooperative game, community detection
