Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1032
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBandara, M-
dc.contributor.authorWeragoda, S-
dc.contributor.authorPiraveenan, M-
dc.contributor.authorKasthurirthna, D-
dc.date.accessioned2022-02-08T10:11:12Z-
dc.date.available2022-02-08T10:11:12Z-
dc.date.issued2018-11-18-
dc.identifier.citationM. Bandara, S. Weragoda, M. Piraveenan and D. Kasthurirthna, "Overlay Community detection using Community Networks," 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018, pp. 680-687, doi: 10.1109/SSCI.2018.8628653.en_US
dc.identifier.isbn978-1-5386-9276-9-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1032-
dc.description.abstractCommunity detection is useful in understanding the structure of a social network. One of the most commonly used algorithms for community detection is the Louvain algorithm, which is based on the Newman-Girman (NG) modularity optimization technique. It is argued that the close spatial proximity of nodes may increase their chance of being in the same community. Variants of the NG modularity measure such as the dist-modularity attempt to normalize the effect of spatial proximity in extracting communities, causing loss of information about the spatially proximate communities. Other variants of NG modularity such as Spatially-near modularity, try to exploit the spatial proximity of nodes to extract communities, causing loss of information on spatially dispersed communities. We propose that `overlay communities' on existing `community networks' can be used to identify spatially dispersed communities, while preserving the information of spatial proximate communities. The community network is formed by reducing a community into a node using a proximity dimension, which are connected by intercommunity links. The overlay communities are the community pairs that have relatively high normalized link strengths, while being relatively apart in selected proximity dimension. We apply this method to the Gowalla and soc-Pokec online social networks and extract the spatially dispersed overlay communities in them. We select the geographical space and the age of the nodes as the proximity dimension of these two networks, respectively. Detecting spatially dispersed overlay communities may be useful in application domains such as indirect marketing, social engineering, counter terrorism and defense.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2018 IEEE Symposium Series on Computational Intelligence (SSCI);Pages 680-687-
dc.subjectOverlayen_US
dc.subjectCommunity detectionen_US
dc.subjectCommunity Networksen_US
dc.titleOverlay community detection using community networksen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/SSCI.2018.8628653en_US
Appears in Collections:Department of Computer Science and Software Engineering -Scopes
Research Papers - Open Access Research
Research Papers - SLIIT Staff Publications

Files in This Item:
File Description SizeFormat 
Overlay_Community_detection_using_Community_Networks.pdf
  Until 2050-12-31
241.4 kBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.