19 citations · 32 across the 5 of their papers we have counts for
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cs.LG2026
Complementary Supervised and Self-Supervised Representations for Out-of-Distribution Graph Learning
Qingying Hao, Zikang Chen, Chuxuan Hu +4
Out-of-distribution (OOD) generalization remains challenging for graph neural networks (GNNs), as graph distributions can vary substantially across time and domains. Supervised and…
cs.LG2022★ 1 cited
EDoG: Adversarial Edge Detection For Graph Neural Networks
Xiaojun Xu, Yue Yu, Hanzhang Wang +3
Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. However, recent studies have shown that GNNs are…
cs.LG2019★ 19 cited
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators
Yunhui Long, Boxin Wang, Zhuolin Yang +4
Recent advances in machine learning have largely benefited from the massive accessible training data. However, large-scale data sharing has raised great privacy concerns. In this w…