Knowledge Commons of Institute of Automation,CAS
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 | |
发表期刊 | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS |
ISSN | 2329-924X |
2022-02-02 | |
页码 | 14 |
摘要 | 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. |
关键词 | Task analysis Optimization Generative adversarial networks Computational modeling Automation Training Standards Embedding generative adversarial network network alignment (NA) optimal transport unsupervised learning |
DOI | 10.1109/TCSS.2022.3144350 |
关键词[WOS] | PARALLEL CONTROL ; SYSTEMS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key R&D Program of China[2018AAA0101502] ; Science and Technology Project of SGCC (State Grid Corporation of China) |
项目资助者 | National Key R&D Program of China ; Science and Technology Project of SGCC (State Grid Corporation of China) |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Cybernetics ; Computer Science, Information Systems |
WOS记录号 | WOS:000751481300001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 人工智能基础理论 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47377 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Wang, Xiao |
作者单位 | 1.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 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 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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HackGAN_Harmonious_C(4053KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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