Advanced community identification model for social networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Amin, Farhan | - |
dc.contributor.author | Choi, Jin-Ghoo | - |
dc.contributor.author | Choi, Gyu Sang | - |
dc.date.accessioned | 2021-08-05T01:40:29Z | - |
dc.date.available | 2021-08-05T01:40:29Z | - |
dc.date.created | 2021-07-30 | - |
dc.date.issued | 2021-07 | - |
dc.identifier.issn | 1546-2218 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81822 | - |
dc.description.abstract | Community detection in social networks is a hard problem because of the size, and the need of a deep understanding of network structure and functions. While several methods with significant effort in this direction have been devised, an outstanding open problem is the unknown number of communities, it is generally believed that the role of influential nodes that are surrounded by neighbors is very important. In addition, the similarity among nodes inside the same cluster is greater than among nodes from other clusters. Lately, the global and localmethods of community detection have been getting more attention. Therefore, in this study, we propose an advanced communitydetection model for social networks in order to identify network communities based on global and local information. Our proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label propagation. This process is conducted with the same color till nodes reach maximum similarity. Finally, the communities are formed, and a clear community graph is displayed to the user. Our proposed model is completely parameter-free, and therefore, no prior information is required, such as the number of communities, etc.We performsimulations and experiments using well-known synthetic and real network benchmarks, and compare them with well-known state-of-the-artmodels. The results prove that our model is efficient in all aspects, because it quickly identifies communities in the network. Moreover, it can easily be used for friendship recommendations or in business recommendation systems. © 2021 Tech Science Press. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Tech Science Press | - |
dc.relation.isPartOf | CMC-COMPUTERS MATERIALS & CONTINUA | - |
dc.title | Advanced community identification model for social networks | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000677642400015 | - |
dc.identifier.doi | 10.32604/cmc.2021.017870 | - |
dc.identifier.bibliographicCitation | CMC-COMPUTERS MATERIALS & CONTINUA, v.69, no.2, pp.1687 - 1707 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85110457328 | - |
dc.citation.endPage | 1707 | - |
dc.citation.startPage | 1687 | - |
dc.citation.title | CMC-COMPUTERS MATERIALS & CONTINUA | - |
dc.citation.volume | 69 | - |
dc.citation.number | 2 | - |
dc.contributor.affiliatedAuthor | Amin, Farhan | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Community detection | - |
dc.subject.keywordAuthor | Complex networks | - |
dc.subject.keywordAuthor | Social network analysis | - |
dc.subject.keywordPlus | Population dynamics | - |
dc.subject.keywordPlus | Community detection | - |
dc.subject.keywordPlus | Community identification | - |
dc.subject.keywordPlus | Global and local informations | - |
dc.subject.keywordPlus | Influential nodes | - |
dc.subject.keywordPlus | Label propagation | - |
dc.subject.keywordPlus | Network communities | - |
dc.subject.keywordPlus | Network structures | - |
dc.subject.keywordPlus | Prior information | - |
dc.subject.keywordPlus | Data reduction | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.