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An Improved Genetic-Based Link Clustering for Overlapping Community Detection

Abstract : The problem of community detection in complex networks has been intensively investigated in recent years. And it was found that the communities of complex networks often overlap with each other. So in this paper, we propose an improved genetic-based link clustering for overlapping community detection. The first, the algorithm changes the node graph into the link graph. The second, the algorithm adopts the genetic algorithm to detect the link communities. The Third, the algorithm transforms the link communities into the node communities. Automatically, the nodes, which are linked with edges belonged to different link communities, will be the overlapping nodes. The last, in order to improve the quality of community detection, we define an effective method to solve the “excessive overlap” problem. The experimental results shows that the proposed algorithm is effective and efficient on both simulate networks and real networks.
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Submitted on : Wednesday, October 11, 2017 - 4:57:57 PM
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Yong Zhou, Guibin Sun. An Improved Genetic-Based Link Clustering for Overlapping Community Detection. 9th International Conference on Intelligent Information Processing (IIP), Nov 2016, Melbourne, VIC, Australia. pp.142-151, ⟨10.1007/978-3-319-48390-0_15⟩. ⟨hal-01614993⟩



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