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dc.creatorChen, Y
dc.creatorZhao, P
dc.creatorLi, P
dc.creatorZhang, K
dc.creatorZhang, J
dc.date.accessioned2021-01-28T23:09:20Z
dc.date.available2021-01-28T23:09:20Z
dc.date.issued2016-04-07
dc.identifier.issn2045-2322
dc.identifier.issn2045-2322
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5127
dc.identifier.other27053090 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5145
dc.description.abstractDetecting communities or clusters in a real-world, networked system is of considerable interest in various fields such as sociology, biology, physics, engineering science, and interdisciplinary subjects, with significant efforts devoted in recent years. Many existing algorithms are only designed to identify the composition of communities, but not the structures. Whereas we believe that the local structures of communities can also shed important light on their detection. In this work, we develop a simple yet effective approach that simultaneously uncovers communities and their centers. The idea is based on the premise that organization of a community generally can be viewed as a high-density node surrounded by neighbors with lower densities, and community centers reside far apart from each other. We propose so-called "community centrality" to quantify likelihood of a node being the community centers in such a landscape, and then propagate multiple, significant center likelihood throughout the network via a diffusion process. Our approach is an efficient linear algorithm, and has demonstrated superior performance on a wide spectrum of synthetic and real world networks especially those with sparse connections amongst the community centers.
dc.format.extent24017-
dc.language.isoen
dc.relation.haspartScientific Reports
dc.relation.isreferencedbySpringer Science and Business Media LLC
dc.rightsCC BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAlgorithms
dc.subjectCommunity Networks
dc.subjectComputer Simulation
dc.subjectHumans
dc.subjectModels, Theoretical
dc.subjectSocial Support
dc.titleFinding Communities by Their Centers
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1038/srep24017
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-01-28T23:09:16Z
refterms.dateFOA2021-01-28T23:09:21Z


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