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Adversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networks

Title
Adversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networks
Author
한경식
Keywords
adversarial learning; balanced triangle; community detection; signed network
Issue Date
2021-12
Publisher
IEEE COMPUTER SOC
Citation
2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021)), v. 2021-Decem, Page. 1150-1155
Abstract
In 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.
URI
https://ieeexplore.ieee.org/document/9679159https://repository.hanyang.ac.kr/handle/20.500.11754/177153
ISSN
1550-4786
DOI
10.1109/ICDM51629.2021.00137
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INTELLIGENCE COMPUTING(데이터사이언스전공) > Articles
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