HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein-Procrustes Learning for Unsupervised Network Alignment
Yang, Linyao1,2; Wang, Xiao1,3; Zhang, Jun4; Yang, Jun4; Xu, Yancai1,3; Hou, Jiachen3,5; Xin, Kejun6; Wang, Fei-Yue1,3
Source PublicationIEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
ISSN2329-924X
2022-02-02
Pages14
Abstract

Network alignment (NA) that identifies equivalent nodes across networks is an effective tool for integrating knowledge from multiple networks. The state-of-the-art NA methods learn inter-network node similarities based on labeled anchor links, which are costly, time-consuming, and difficult to acquire. Therefore, a few unsupervised network alignment (UNA) methods propose solving NA problems without anchor links. However, most existing UNA methods rely on discriminative attributes to capture nodes' similarities and are hard to obtain optimal one-to-one alignments. Toward these issues, this article proposes a novel method named HackGAN to solve the UNA problem solely based on the structural information. Specifically, HackGAN represents nodes with embeddings based on an unsupervised graph neural network (GNN) to capture their global and local structural features. After that, it initializes mapping functions to transform the embedding spaces of different networks into the same vector space by iteratively solving the Wasserstein-Procrustes problem. The mapping functions are then refined by an adversarial model with cycle-consistency and Sinkhorn distance losses to obtain optimized one-to-one mappings. Based on the distances between mapped embeddings, accurate and robust results are obtained with a collective alignment algorithm. Experimental comparisons on both synthetic and real-world datasets demonstrate the superiority of HackGAN.

KeywordTask analysis Optimization Generative adversarial networks Computational modeling Automation Training Standards Embedding generative adversarial network network alignment (NA) optimal transport unsupervised learning
DOI10.1109/TCSS.2022.3144350
WOS KeywordPARALLEL CONTROL ; SYSTEMS
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2018AAA0101502] ; Science and Technology Project of SGCC (State Grid Corporation of China)
Funding OrganizationNational Key R&D Program of China ; Science and Technology Project of SGCC (State Grid Corporation of China)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Cybernetics ; Computer Science, Information Systems
WOS IDWOS:000751481300001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification人工智能基础理论
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47377
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorWang, Xiao
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Qingdao Acad Intelligent Ind, Qingdao 266200, Peoples R China
4.Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Peoples R China
5.Macau Univ Sci & Technol, Inst Syst Engn, Taipa 999078, Macao, Peoples R China
6.Nanjing Joinmap Data Res Inst, Nanjing 211100, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yang, Linyao,Wang, Xiao,Zhang, Jun,et al. HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein-Procrustes Learning for Unsupervised Network Alignment[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2022:14.
APA Yang, Linyao.,Wang, Xiao.,Zhang, Jun.,Yang, Jun.,Xu, Yancai.,...&Wang, Fei-Yue.(2022).HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein-Procrustes Learning for Unsupervised Network Alignment.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,14.
MLA Yang, Linyao,et al."HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein-Procrustes Learning for Unsupervised Network Alignment".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2022):14.
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