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A community merger of optimization algorithm to extract overlapping communities in networks

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Li, Q, Zhong, J, Li, Q, Wang, C and Cao, Z ORCID: 0000-0003-3656-0328 2018 , 'A community merger of optimization algorithm to extract overlapping communities in networks' , IEEE Access, vol. 7 , pp. 3994-4005 , doi: 10.1109/ACCESS.2018.2884447.

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Abstract

A community in networks is a subset of vertices primarily connecting internal components, yet less connecting to the external vertices. The existing algorithms aim to extract communities of the topological features in networks. However, the edges of practical complex networks involving a weight that represents the tightness degree of connection and robustness, which leads a significant influence on the accuracy of community detection. In our study, we propose an overlapping community detection method based on the seed expansion strategy applying to both the unweighted and the weighted networks, called OCSE. First, it redefines the edge weight and the vertex weight depending on the influence of the network topology and the original edge weight, and then selects the seed vertices and updates the edges weight. Comparisons between OCSE approach and existing community detection methods on synthetic and real-world networks, the results of the experiment show that our proposed approach has the significantly better performance in terms of the accuracy.

Item Type: Article
Authors/Creators:Li, Q and Zhong, J and Li, Q and Wang, C and Cao, Z
Keywords: overlapping community detection, complex network, weighted network, dense subgraph, data
Journal or Publication Title: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 2169-3536
DOI / ID Number: 10.1109/ACCESS.2018.2884447
Copyright Information:

Copyright 2018 IEEE.

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