An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A Fast Algorithm to Find Overlapping Communities in Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Overlapped community detection in complex networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Detecting Highly Overlapping Communities with Model-Based Overlapping Seed Expansion
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Detecting Link Communities in Massive Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Efficient identification of overlapping communities
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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There is a surge of community detection study on complex network analysis in recent years, since communities often play important roles in network systems. However, many real networks have more complex overlapping community structures. This paper proposes a novel algorithm to discover overlapping communities. Different from conventional algorithms based on node clustering, the proposed algorithm is based on link clustering. Since links usually represent unique relations among nodes, the link clustering will discover groups of links that have the same characteristics. Thus nodes naturally belong to multiple communities. The algorithm applies genetic operation to cluster on links. An effective encoding schema is designed and the number of communities can be automatically determined. Experiments on both artificial networks and real networks validate the effectiveness and efficiency of the proposed algorithm.