42 citations · 98 across the 8 of their papers we have counts for
7 papers · 1 filter
Graph Fairness Learning under Distribution Shifts
Yibo Li, Xiao Wang, Yujie Xing +4
Graph neural networks (GNNs) have achieved remarkable performance on graph-structured data. However, GNNs may inherit prejudice from the training data and make discriminatory predi…
Graph Contrastive Invariant Learning from the Causal Perspective
Yanhu Mo, Xiao Wang, Shaohua Fan +1
Graph contrastive learning (GCL), learning the node representation by contrasting two augmented graphs in a self-supervised way, has attracted considerable attention. GCL is usuall…
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…
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…
Debiased Graph Neural Networks with Agnostic Label Selection Bias
Shaohua Fan, Xiao Wang, Chuan Shi +3
Most existing Graph Neural Networks (GNNs) are proposed without considering the selection bias in data, i.e., the inconsistent distribution between the training set with test set.…
Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
Shaohua Fan, Xiao Wang, Chuan Shi +2
Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training and testing graphs, inducing the degeneration of the generalization…
Decorrelated Clustering with Data Selection Bias
Xiao Wang, Shaohua Fan, Kun Kuang +3
Most of existing clustering algorithms are proposed without considering the selection bias in data. In many real applications, however, one cannot guarantee the data is unbiased. S…