96 citations · 345 across the 13 of their papers we have counts for
16 papers · 1 filter
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…
Self-Explainable Graph Neural Networks for Link Prediction
Huaisheng Zhu, Dongsheng Luo, Xianfeng Tang +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance for link prediction. However, GNNs suffer from poor interpretability, which limits their adoptions in critic…
DiP-GNN: Discriminative Pre-Training of Graph Neural Networks
Simiao Zuo, Haoming Jiang, Qingyu Yin +3
Graph neural network (GNN) pre-training methods have been proposed to enhance the power of GNNs. Specifically, a GNN is first pre-trained on a large-scale unlabeled graph and then…
Condensing Graphs via One-Step Gradient Matching
Wei Jin, Xianfeng Tang, Haoming Jiang +4
As training deep learning models on large dataset takes a lot of time and resources, it is desired to construct a small synthetic dataset with which we can train deep learning mode…
Task-Agnostic Graph Explanations
Yaochen Xie, Sumeet Katariya, Xianfeng Tang +4
Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph-structured data. Due to their broad applications, there is an increasing need to develop tools to explai…
Jointly Attacking Graph Neural Network and its Explanations
Wenqi Fan, Wei Jin, Xiaorui Liu +7
Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are highly vulnerable to adve…