Correntropy-Induced Wasserstein GCN: Learning Graph Embedding via Domain Adaptation | |
Wei Wang1; Gaowei Zhang1; Hongyong Han1; Chi Zhang2 | |
刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
![]() |
2023 | |
页码 | 3980-3993 |
英文摘要 | Graph embedding aims at learning vertex representations in a low-dimensional space by distilling information from a complex-structured graph. Recent efforts in graph embedding have been devoted to generalizing the representations from the trained graph in a source domain to the new graph in a different target domain based on information transfer. However, when the graphs are contaminated by unpredictable and complex noise in practice, this transfer problem is quite challenging because of the need to extract helpful knowledge from the source graph and to reliably transfer knowledge to the target graph. This paper puts forward a two-step correntropy-induced Wasserstein GCN (graph convolutional network, or CW-GCN for short) architecture to facilitate the robustness in cross-graph embedding. In the first step, CW-GCN originally investigates correntropy-induced loss in GCN, which places bounded and smooth losses on the noisy nodes with incorrect edges or attributes. Consequently, helpful information are extracted only from clean nodes in the source graph. In the second step, a novel Wasserstein distance is introduced to measure the difference in marginal distributions between graphs, avoiding the negative influence of noise. Afterwards, CW-GCN maps the target graph to the same embedding space as the source graph by minimizing the Wasserstein distance, and thus the knowledge preserved in the first step is expected to be reliably transferred to assist the target graph analysis tasks. Extensive experiments demonstrate the significant superiority of CW-GCN over state-of-the-art methods in different noisy environments |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/57646] ![]() |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Chi Zhang |
作者单位 | 1.Beijing University of Posts and Telecommunications 2.Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Wei Wang,Gaowei Zhang,Hongyong Han,et al. Correntropy-Induced Wasserstein GCN: Learning Graph Embedding via Domain Adaptation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023:3980-3993. |
APA | Wei Wang,Gaowei Zhang,Hongyong Han,&Chi Zhang.(2023).Correntropy-Induced Wasserstein GCN: Learning Graph Embedding via Domain Adaptation.IEEE TRANSACTIONS ON IMAGE PROCESSING,3980-3993. |
MLA | Wei Wang,et al."Correntropy-Induced Wasserstein GCN: Learning Graph Embedding via Domain Adaptation".IEEE TRANSACTIONS ON IMAGE PROCESSING (2023):3980-3993. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论