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Spectral Adversarial Training for Robust Graph Neural Network
Jintang Li, Jiaying Peng, Liang Chen +3
Recent studies demonstrate that Graph Neural Networks (GNNs) are vulnerable to slight but adversarially designed perturbations, known as adversarial examples. To address this issue…
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Bingzhe Wu, Jintang Li, Junchi Yu +17
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite t…
A Survey of Deep Learning Models for Structural Code Understanding
Ruoting Wu, Yuxin Zhang, Qibiao Peng +2
In recent years, the rise of deep learning and automation requirements in the software industry has elevated Intelligent Software Engineering to new heights. The number of approach…
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation
Yuansheng Wang, Wangbin Sun, Kun Xu +3
Graph contrastive learning (GCL), as a popular approach to graph self-supervised learning, has recently achieved a non-negligible effect. To achieve superior performance, the major…
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
Jintang Li, Ruofan Wu, Wangbin Sun +6
The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding. However, there is a lack of theoretical understand…
GUARD: Graph Universal Adversarial Defense
Jintang Li, Jie Liao, Ruofan Wu +5
Graph convolutional networks (GCNs) have been shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in secu…
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
Jintang Li, Bingzhe Wu, Chengbin Hou +5
Deep graph learning (DGL) has achieved remarkable progress in both business and scientific areas ranging from finance and e-commerce to drug and advanced material discovery. Despit…
Neighboring Backdoor Attacks on Graph Convolutional Network
Liang Chen, Qibiao Peng, Jintang Li +4
Backdoor attacks have been widely studied to hide the misclassification rules in the normal models, which are only activated when the model is aware of the specific inputs (i.e., t…