4 papers
A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions
Chenghua Gong, Yao Cheng, Jianxiang Yu +4
Graphs are structured data that models complex relations between real-world entities. Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar fe…
Prioritized Propagation in Graph Neural Networks
Yao Cheng, Minjie Chen, Xiang Li +2
Graph neural networks (GNNs) have recently received significant attention. Learning node-wise message propagation in GNNs aims to set personalized propagation steps for different n…
DropMix: Better Graph Contrastive Learning with Harder Negative Samples
Yueqi Ma, Minjie Chen, Xiang Li
While generating better negative samples for contrastive learning has been widely studied in the areas of CV and NLP, very few work has focused on graph-structured data. Recently,…
Resurrecting Label Propagation for Graphs with Heterophily and Label Noise
Yao Cheng, Caihua Shan, Yifei Shen +3
Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels…