Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks

dc.creatorCui, Yaozu
dc.creatorWang, Xingyuan
dc.creatorEustace, Justine
dc.date2020-12-10T06:28:49Z
dc.date2020-12-10T06:28:49Z
dc.date2014
dc.date.accessioned2022-10-20T13:47:42Z
dc.date.available2022-10-20T13:47:42Z
dc.descriptionAbstract. Full text article available at https://doi.org/10.1016/j.physa.2014.08.050
dc.descriptionCommunity structure is a common phenomenon in complex networks, and it has been shown that some communities in complex networks often overlap each other. So in this paper we propose a new algorithm to detect overlapping community structure in complex networks. To identify the overlapping community structure, our algorithm firstly extracts fully connected sub-graphs which are maximal sub-graphs from original networks. Then two maximal sub-graphs having the key pair-vertices can be merged into a new larger sub-graph using some belonging degree functions. Furthermore we extend the modularity function to evaluate the proposed algorithm. In addition, overlapping nodes between communities are founded successfully. Finally we report the comparison between the modularity and the computational complexity of the proposed algorithm with some other existing algorithms. The experimental results show that the proposed algorithm gives satisfactory results.
dc.identifierCui, Y., Wang, X., & Eustace, J. (2014). Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks. Physica A: Statistical Mechanics and its Applications, 416, 198-207.
dc.identifierhttp://hdl.handle.net/20.500.12661/2639
dc.identifier.urihttp://hdl.handle.net/20.500.12661/2639
dc.languageen
dc.publisherElsevier
dc.subjectComplex networks
dc.subjectMaximal sub-graph
dc.subjectBelonging degree
dc.subjectCommunity structure
dc.subjectOverlapping community
dc.subjectComputational complexity
dc.subjectCommunity
dc.subjectNetwork
dc.titleDetecting community structure via the maximal sub-graphs and belonging degrees in complex networks
dc.typeArticle

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