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Searching overlapping communities for group query

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In most real life networks such as social networks and biology networks, a node often involves in multiple overlapping communities. Thus, overlapping community discovery has drawn a great deal of attention and there is a lot of research on it. However, most work has focused on community detection, which takes the whole network as input and derives all communities at one time. Community detection can only be used in offline analysis of networks and it is quite costly, not flexible and can not support dynamically evolving networks. Online community search which only finds overlapping communities containing a given node is a flexible and light-weight solution, and also supports dynamic graphs very well. However, in some scenarios, it requires overlapping community search for group query, which means that the input is a set of nodes instead of one single node. To solve this problem, we propose an overlapping community search framework for group query, including both exact and heuristic solutions. The heuristic solution has four strategies, some of which are adjustable and self-adaptive. We propose two parameters node degree and discovery power to trade off the efficiency and quality of the heuristic strategies, in order to make them satisfy different application requirements. Comprehensive experiments are conducted and demonstrate the efficiency and quality of both exact and heuristic solutions.

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This work is supported by the National Basic Research 973 Program of China under Grant No.2012CB316201, the National Natural Science Foundation of China under Grant Nos. 61033007, 61472070, and the Fundamental Research Funds for the Central Universities (N120816001).

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Correspondence to Jing Shan.

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This paper is an extended version of our previous conference paper [24].

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Shan, J., Shen, D., Nie, T. et al. Searching overlapping communities for group query. World Wide Web 19, 1179–1202 (2016). https://doi.org/10.1007/s11280-015-0378-5

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  • Community search
  • Social networks
  • Graph mining