9 citations · 15 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 1 cited
Finding Diverse and Predictable Subgraphs for Graph Domain Generalization
Junchi Yu, Jian Liang, Ran He
This paper focuses on out-of-distribution generalization on graphs where performance drops due to the unseen distribution shift. Previous graph domain generalization works always r…
cs.LG2022★ 9 cited
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Bingzhe Wu, Jintang Li, Junchi Yu +17
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite t…
cs.LG2021★ 5 cited
Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows
Junchi Yu, Tingyang Xu, Ran He
As Graph Neural Networks (GNNs) are widely adopted in digital pathology, there is increasing attention to developing explanation models (explainers) of GNNs for improved transparen…