Journal
SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA
Volume -, Issue -, Pages 991-1002Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2588555.2612179
Keywords
Community search; Social networks; Graph mining
Categories
Funding
- National NSFC [61003001, 61170006, 61171132, 61033010]
- NSF of Jiangsu Province [BK2010280]
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Community search is important in social network analysis. For a given vertex in a graph, the goal is to find the best community the vertex belongs to. Intuitively, the best community for a given vertex should be in the vicinity of the vertex. However, existing solutions use global search to find the best community. These algorithms, although straight-forward, are very costly, as all vertices in the graph may need to be visited. In this paper, we propose a local search strategy, which searches in the neighborhood of a vertex to find the best community for the vertex. We show that, because the minimum degree measure used to evaluate the goodness of a community is not monotonic, designing efficient local search solutions is a very challenging task. We present theories and algorithms of local search to address this challenge. The efficiency of our local search strategy is verified by extensive experiments on both synthetic networks and a variety of real networks with millions of nodes.
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