CR-Graph: Community Reinforcement for Accurate Community Detection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Yoonsuk | - |
dc.contributor.author | Lee, Jun Seok | - |
dc.contributor.author | Shin, Won-Yong | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.date.accessioned | 2022-07-07T14:32:05Z | - |
dc.date.available | 2022-07-07T14:32:05Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2020-10 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/144955 | - |
dc.description.abstract | In this paper, we present CR-Graph (community reinforcement on graphs), a novel method that helps existing algorithms to perform more-accurate community detection (CD). Toward this end, CR-Graph strengthens the community structure of a given original graph by adding non-existent predicted intra-community edges and deleting existing predicted inter-community edges. To design CR-Graph, we propose the following two strategies: (1) predicting intra-community and inter-community edges (i.e., the type of edges) and (2) determining the amount of edges to be added/deleted. To show the effectiveness of CR-Graph, we conduct extensive experiments with various CD algorithms on 7 synthetic and 4 real-world graphs. The results demonstrate that CR-Graph improves the accuracy of all underlying CD algorithms universally and consistently. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | CR-Graph: Community Reinforcement for Accurate Community Detection | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/3340531.3412145 | - |
dc.identifier.scopusid | 2-s2.0-85095863158 | - |
dc.identifier.bibliographicCitation | International Conference on Information and Knowledge Management, Proceedings, pp.2077 - 2080 | - |
dc.relation.isPartOf | International Conference on Information and Knowledge Management, Proceedings | - |
dc.citation.title | International Conference on Information and Knowledge Management, Proceedings | - |
dc.citation.startPage | 2077 | - |
dc.citation.endPage | 2080 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Knowledge management | - |
dc.subject.keywordPlus | Population dynamics | - |
dc.subject.keywordPlus | Reinforcement | - |
dc.subject.keywordPlus | CD-algorithms | - |
dc.subject.keywordPlus | Community detection | - |
dc.subject.keywordPlus | Community structures | - |
dc.subject.keywordPlus | Real-world graphs | - |
dc.subject.keywordPlus | Graph algorithms | - |
dc.subject.keywordAuthor | community detection | - |
dc.subject.keywordAuthor | community reinforcement | - |
dc.subject.keywordAuthor | inter-community edges | - |
dc.subject.keywordAuthor | intra-community edges | - |
dc.subject.keywordAuthor | preprocessing | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3340531.3412145 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
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.