87 citations · 305 across the 34 of their papers we have counts for
4 papers · 1 filter
Certifiably Robust Graph Contrastive Learning
Minhua Lin, Teng Xiao, Enyan Dai +2
Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph representation learning method. However, it has been shown that GCL is vulnerable to adversarial attack…
Shape-aware Graph Spectral Learning
Junjie Xu, Enyan Dai, Dongsheng Luo +2
Spectral Graph Neural Networks (GNNs) are gaining attention for their ability to surpass the limitations of message-passing GNNs. They rely on supervision from downstream tasks to…
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Enyan Dai, Limeng Cui, Zhengyang Wang +5
Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured data. However, recent works show that GNNs are vulnerable to adversarial attacks which can foo…
Unnoticeable Backdoor Attacks on Graph Neural Networks
Enyan Dai, Minhua Lin, Xiang Zhang +1
Graph Neural Networks (GNNs) have achieved promising results in various tasks such as node classification and graph classification. Recent studies find that GNNs are vulnerable to…