Community detection using local neighborhood in complex networks

dc.contributor.authorEustace, Justine
dc.contributor.authorWang, Xingyuan
dc.contributor.authorCui, Yaozu
dc.date.accessioned2021-05-05T08:13:52Z
dc.date.available2021-05-05T08:13:52Z
dc.date.issued2015
dc.descriptionAbstract. Full text article available at https://doi.org/10.1016/j.physa.2015.05.044en_US
dc.description.abstractIt is common to characterize community structure in complex networks using local neighborhood. Existing related methods fail to estimate the accurate number of nodes present in each community in the network. In this paper a community detection algorithm using local community neighborhood ratio function is proposed. The proposed algorithm predicts vertex association to a specific community using visited node overlapped neighbors. In the beginning, the algorithm detects local communities; then through iterations and local neighborhood ratio function, final communities are detected by merging close related local communities. Analysis of simulation results on real and artificial networks shows the proposed algorithm detects well defined communities in both networks by wide margin.en_US
dc.identifier.citationEustace, J., Wang, X., & Cui, Y. (2015). Community detection using local neighborhood in complex networks. Physica A: Statistical Mechanics and its Applications, 436, 665-677.en_US
dc.identifier.otherDOI: 10.1016/j.physa.2015.05.044
dc.identifier.urihttp://hdl.handle.net/20.500.12661/2939
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectComplex networksen_US
dc.subjectLocal neighborhooden_US
dc.subjectNeighborhood ratioen_US
dc.subjectCommunity detection algorithmsen_US
dc.subjectLocal community neighborhooden_US
dc.subjectData miningen_US
dc.subjectBehavior scienceen_US
dc.subjectCommunity structureen_US
dc.subjectAlgorithmsen_US
dc.titleCommunity detection using local neighborhood in complex networksen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Eustace and Wang.pdf
Size:
82.74 KB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Collections