1 citations · 1 across the 3 of their papers we have counts for
4 papers
GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning
Chuang Liu, Zelin Yao, Xueqi Ma +4
Graph self-supervised learning typically relies on large-scale unlabeled datasets, heavily inflating computational costs. However, empirical evidence suggests that these datasets c…
From Subtle to Significant: Prompt-Driven Self-Improving Optimization in Test-Time Graph OOD Detection
Luzhi Wang, Xuanshuo Fu, He Zhang +3
Graph Out-of-Distribution (OOD) detection aims to identify whether a test graph deviates from the distribution of graphs observed during training, which is critical for ensuring th…
Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
Xiaobao Wang, Ruoxiao Sun, Yujun Zhang +4
Graph Neural Networks (GNNs) have demonstrated strong performance across tasks such as node classification, link prediction, and graph classification, but remain vulnerable to back…
GOODAT: Towards Test-time Graph Out-of-Distribution Detection
Luzhi Wang, Dongxiao He, He Zhang +5
Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. While GNNs excel in scenarios where the testing data shares the distri…