58 citations · 107 across the 33 of their papers we have counts for
7 papers · 1 filter
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…
Are All Edges Necessary? A Unified Framework for Graph Purification
Zishan Gu, Jintang Li, Liang Chen
Graph Neural Networks (GNNs) as deep learning models working on graph-structure data have achieved advanced performance in many works. However, it has been proved repeatedly that,…
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
Jintang Li, Zhouxin Yu, Zulun Zhu +6
Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However…
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…
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…
Spiking Graph Convolutional Networks
Zulun Zhu, Jiaying Peng, Jintang Li +3
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemente…
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…