1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116 2.Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116 3.Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou 350116
Abstract：The community structures of social networks in the real world are always varying with nodes and edges of social networks increasing or disappearing dynamically as time goes by. In this paper, an incremental dynamic community detection algorithm based on density clustering is proposed. Firstly, the initial communities are generated according to the improved DBSCAN algorithm. Then, an index of edge variation rate is proposed and it is combined with the cosine similarity index to determine the community belonging adjustment process of the nodes whose neighbors vary in adjacent moment. In addition, both direct and indirect neighbor nodes are taken into account during the calculation of community belongingness.Finally, the communities are merged by iteratively updating the modularity gain to reduce the interference of noise communities. Experimental results on artificial datasets and real networks show that the proposed algorithm effectively copes with the variation of the network structures and incremental calculation cumulative errors with a low time complexity.
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2018, Vol. 31 
