Exploring Local Community Structures in Large Networks
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
FRINGE: a new approach to the detection of overlapping communities in graphs
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
Computing communities in large networks using random walks
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
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Currently, the detection of global community structure in networks has gathered a lot of attention. Most of the methods need global knowledge of the graphs which would be unrealistic to get when the graphs are too large or evolve too quickly. Moreover, sometimes we are only interested in the community structures of some given nodes, not all nodes. So detecting the community of a given node i.e. local community detection is more appropriate. Most of the proposed solutions for local community detection built upon the source nodes are sensitive to the position of source nodes. In this paper, we propose a method to detect local community of a given node by finding the core node of the community firstly. Then expand the core node's cliques to get community of the given node. We validate our method on real-world networks whose community structures are available. The result shows that our method can get high recall and precision score and is quite effective and flexible to identify local communities, irrespective of the source node position.