2 papers
cs.LG2025
A Recipe for Causal Graph Regression: Confounding Effects Revisited
Yujia Yin, Tianyi Qu, Zihao Wang +1
Through recognizing causal subgraphs, causal graph learning (CGL) has risen to be a promising approach for improving the generalizability of graph neural networks under out-of-dist…
cs.LG2025
Catch Causal Signals from Edges for Label Imbalance in Graph Classification
Fengrui Zhang, Yujia Yin, Hongzong Li +2
Despite significant advancements in causal research on graphs and its application to cracking label imbalance, the role of edge features in detecting the causal effects within grap…