42 citations · 84 across the 4 of their papers we have counts for
4 papers
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.…
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Xiao Wang, Deyu Bo, Chuan Shi +3
Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representat…
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…