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dc.contributor.author한경식-
dc.date.accessioned2022-11-22T01:52:44Z-
dc.date.available2022-11-22T01:52:44Z-
dc.date.issued2021-12-
dc.identifier.citation2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021)), v. 2021-Decem, Page. 1150-1155en_US
dc.identifier.issn1550-4786en_US
dc.identifier.urihttps://ieeexplore.ieee.org/document/9679159en_US
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/177153-
dc.description.abstractIn this paper, we propose a framework for embedding-based community detection on signed networks. It first represents all the nodes of a signed network as vectors in low-dimensional embedding space and conducts a clustering algorithm (e.g., k-means) on vectors, thereby detecting a community structure in the network. When performing the embedding process, our framework learns only the edges belonging to balanced triangles whose edge signs follow the balance theory, significantly excluding noise edges in learning. To address the sparsity of balanced triangles in a signed network, our framework learns not only the edges in balanced real-triangles but those in balanced virtual-triangles that are produced by our generator. Finally, our framework employs adversarial learning to generate more-realistic balanced virtual-triangles with less noise edges. Through extensive experiments using seven real-world networks, we validate the effectiveness of (1) learning edges belonging to balanced real/virtual-triangles and (2) employing adversarial learning for signed network embedding. We show that our framework consistently and significantly outperforms the stateof-the-art community detection methods in all datasets.en_US
dc.description.sponsorshipThis research was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (NRF-2020R1A2B5B03001960 and 2018R1A5A7059549) and the Institute of Information & Communications Technology Planning & Evaluation(IITP) grant funded by the MSIT (No. 2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)).en_US
dc.languageenen_US
dc.publisherIEEE COMPUTER SOCen_US
dc.subjectadversarial learningen_US
dc.subjectbalanced triangleen_US
dc.subjectcommunity detectionen_US
dc.subjectsigned networken_US
dc.titleAdversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networksen_US
dc.typeArticleen_US
dc.relation.volume2021-Decem-
dc.identifier.doi10.1109/ICDM51629.2021.00137en_US
dc.relation.page1150-1155-
dc.relation.journal2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021))-
dc.contributor.googleauthorKang, Yoonsuk-
dc.contributor.googleauthorLee, Woncheol-
dc.contributor.googleauthorLee, Yeon-Chang-
dc.contributor.googleauthorHan, Kyungsik-
dc.contributor.googleauthorKim, Sang-Wook-
dc.sector.campusS-
dc.sector.daehak공과대학-
dc.sector.department데이터사이언스전공-
dc.identifier.pidkyungsikhan-
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COLLEGE OF ENGINEERING[S](공과대학) > INTELLIGENCE COMPUTING(데이터사이언스전공) > Articles
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