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Self-Training Enhanced: Network Embedding and Overlapping Community Detection With Adversarial Learning
Chen, Junyang1; Gong, Zhiguo2; Mo, Jiqian2; Wang, Wei3; Wang, Wei4; Wang, Cong5; Dong, Xiao6; Liu, Weiwen7; Wu, Kaishun1
2022-11-01
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
Volume33Issue:11Pages:6737-6748
Abstract

Network embedding (NE) aims to encode the relations of vertices into a low-dimensional space. After NE, we can obtain the learned vectors of vertices that preserve the proximity of network structures for subsequent applications, e.g., vertex classification and link prediction. In existing NE models, they usually exploit the skip-gram with a negative sampling method to optimize their objective functions. Generally, this method learns the vertex representation only from the local connectivity of vertices (i.e., neighbors). However, there is a larger scope of vertex connectivity in real-world scenarios: a vertex may have multifaceted aspects and should belong to overlapping communities. Taking a social network as the overlapping example, a user may subscribe to the channels of politics, economy, and sports simultaneously, but the politics share more common attributes with the economy and less with the sports. In this article, we propose an adversarial learning approach (ACNE) for modeling overlapping communities of vertices. Specifically, we map the association between communities and vertices into an embedding space. Moreover, we take further research on enhancing our ACNE with the following two operations. First, in the initialization stage, we adopt a walking strategy with perception to obtain paths containing more possible boundary vertices to improve overlapping community detection. Then, after representation learning with ACNE, we use soft community assignments from a simple classifier as supervision to update the weights of ACNE. This self-training mechanism referred to as ACNE-ST can help ACNE to achieve better performance. Experimental results demonstrate that the proposed methods, including ACNE and ACNE-ST, can outperform the state-of-the-art models on the subsequent tasks of vertex classification and overlapping community detection.

KeywordAdversarial Learning Network Embedding (Ne) Overlapping Community Detection Self-training
DOI10.1109/TNNLS.2021.3083318
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000733527700001
Scopus ID2-s2.0-85140932646
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Cited Times [WOS]:5   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Shenzhen University, College of Computer Science and Software Engineering, Shenzhen, 518057, China
2.University of Macau, State Key Laboratory of Internet of Things for Smart City, Department of Computer Information Science, Taipa, Macao
3.Sun Yat-sen University, School of Intelligent Systems Engineering, Shenzhen, 518107, China
4.Dalian University of Technology, School of Information Science and Engineering, Dalian, 116000, China
5.The Hong Kong Polytechnic University, Department of Computing, Hong Kong
6.Sun Yat-sen University, School of Artificial Intelligence, Shenzhen, 518107, China
7.The Chinese University of Hong Kong, Department of Computer Science and Engineering, Hong Kong
Recommended Citation
GB/T 7714
Chen, Junyang,Gong, Zhiguo,Mo, Jiqian,et al. Self-Training Enhanced: Network Embedding and Overlapping Community Detection With Adversarial Learning[J]. IEEE Transactions on Neural Networks and Learning Systems,2022,33(11):6737-6748.
APA Chen, Junyang,Gong, Zhiguo,Mo, Jiqian,Wang, Wei,Wang, Wei,Wang, Cong,Dong, Xiao,Liu, Weiwen,&Wu, Kaishun.(2022).Self-Training Enhanced: Network Embedding and Overlapping Community Detection With Adversarial Learning.IEEE Transactions on Neural Networks and Learning Systems,33(11),6737-6748.
MLA Chen, Junyang,et al."Self-Training Enhanced: Network Embedding and Overlapping Community Detection With Adversarial Learning".IEEE Transactions on Neural Networks and Learning Systems 33.11(2022):6737-6748.
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