38 citations · 190 across the 17 of their papers we have counts for
4 papers · 1 filter
Learning Invariant Representations of Graph Neural Networks via Cluster Generalization
Donglin Xia, Xiao Wang, Nian Liu +1
Graph neural networks (GNNs) have become increasingly popular in modeling graph-structured data due to their ability to learn node representations by aggregating local structure in…
Minimum Topology Attacks for Graph Neural Networks
Mengmei Zhang, Xiao Wang, Chuan Shi +3
With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial topology attacks has received significant attention. Although many attack methods have be…
Graph Fairness Learning under Distribution Shifts
Yibo Li, Xiao Wang, Yujie Xing +4
Graph neural networks (GNNs) have achieved remarkable performance on graph-structured data. However, GNNs may inherit prejudice from the training data and make discriminatory predi…
Graph Contrastive Invariant Learning from the Causal Perspective
Yanhu Mo, Xiao Wang, Shaohua Fan +1
Graph contrastive learning (GCL), learning the node representation by contrasting two augmented graphs in a self-supervised way, has attracted considerable attention. GCL is usuall…