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Identity-Preserving Adversarial Training for Robust Network Embedding

文献类型：期刊论文

作者Cen, Ke-Ting2,3; Shen, Hua-Wei1,2,3; Cao, Qi2; Xu, Bing-Bing2; Cheng, Xue-Qi3,4
刊名JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
出版日期2024-02-01
卷号39期号:1页码:177-191
关键词network embedding identity-preserving adversarial training adversarial the example
ISSN号1000-9000
DOI10.1007/s11390-023-2256-4
英文摘要Network embedding, as an approach to learning low-dimensional representations of nodes, has been proved extremely useful in many applications, e.g., node classification and link prediction. Unfortunately, existing network embedding models are vulnerable to random or adversarial perturbations, which may degrade the performance of network embedding when being applied to downstream tasks. To achieve robust network embedding, researchers introduce adversarial training to regularize the embedding learning process by training on a mixture of adversarial examples and original examples. However, existing methods generate adversarial examples heuristically, failing to guarantee the imperceptibility of generated adversarial examples, and thus limit the power of adversarial training. In this paper, we propose a novel method Identity-Preserving Adversarial Training (IPAT) for network embedding, which generates imperceptible adversarial examples with explicit identity-preserving regularization. We formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class, and we encourage each adversarial example to be discriminated as the class of its original node. Extensive experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.
资助项目National Natural Science Foundation of China[U21B2046] ; National Natural Science Foundation of China[62102402] ; National Key Research and Development Program of China[2020AAA0105200] ; CCF-Tencent Open Research Fund[RAGR20210108] ; Beijing Academy of Artificial Intelligence (BAAI)
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001200760400007
出版者SPRINGER SINGAPORE PTE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/38980]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Shen, Hua-Wei
作者单位1.Beijing Acad Artificial Intelligence, Beijing 100000, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Data Intelligence Syst Res Ctr, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 101480, Peoples R China
4.Chinese Acad Sci, Key Lab Network Data Sci & Technol, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Cen, Ke-Ting,Shen, Hua-Wei,Cao, Qi,et al. Identity-Preserving Adversarial Training for Robust Network Embedding[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2024,39(1):177-191.
APA Cen, Ke-Ting,Shen, Hua-Wei,Cao, Qi,Xu, Bing-Bing,&Cheng, Xue-Qi.(2024).Identity-Preserving Adversarial Training for Robust Network Embedding.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,39(1),177-191.
MLA Cen, Ke-Ting,et al."Identity-Preserving Adversarial Training for Robust Network Embedding".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39.1(2024):177-191.

入库方式： OAI收割

来源：计算技术研究所

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