3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting
Lirong Wu, Haitao Lin, Guojiang Zhao +2
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on message passing to perform fea…
cs.CR2022★ 3 cited
Are Gradients on Graph Structure Reliable in Gray-box Attacks?
Zihan Liu, Yun Luo, Lirong Wu +3
Graph edge perturbations are dedicated to damaging the prediction of graph neural networks by modifying the graph structure. Previous gray-box attackers employ gradients from the s…