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


Li, Q and Zhong, J and Li, Q and Wang, C and Cao, Z, A community merger of optimization algorithm to extract overlapping communities in networks, IEEE Access, 7 pp. 3994-4005. ISSN 2169-3536 (2019) [Refereed Article]


Copyright Statement

Copyright 2018 IEEE.

DOI: doi:10.1109/ACCESS.2018.2884447


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 Details

Item Type:Refereed Article
Keywords:overlapping community detection, complex network, weighted network, dense subgraph, data
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:131574
Year Published:2019 (online first 2018)
Web of Science® Times Cited:7
Deposited By:Information and Communication Technology
Deposited On:2019-03-23
Last Modified:2019-05-13
Downloads:180 View Download Statistics

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