519 citations · 1.4k across the 30 of their papers we have counts for
8 papers · 1 filter
Directed Acyclic Graph Structure Learning from Dynamic Graphs
Shaohua Fan, Shuyang Zhang, Xiao Wang +1
Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights…
Uncovering the Structural Fairness in Graph Contrastive Learning
Ruijia Wang, Xiao Wang, Chuan Shi +1
Recent studies show that graph convolutional network (GCN) often performs worse for low-degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed deg…
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
Nian Liu, Xiao Wang, Deyu Bo +2
Graph Contrastive Learning (GCL), learning the node representations by augmenting graphs, has attracted considerable attentions. Despite the proliferation of various graph augmenta…
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
Shaohua Fan, Xiao Wang, Yanhu Mo +2
Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by learning the correlation between the input graphs and labels. However, by presenting a graph classification…
Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network
Tianyu Zhao, Cheng Yang, Yibo Li +7
Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due…
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift
Hongrui Liu, Binbin Hu, Xiao Wang +3
Graph Convolutional Networks (GCNs) have recently attracted vast interest and achieved state-of-the-art performance on graphs, but its success could typically hinge on careful trai…