← Search

Yanhu Mo

2 accepted papers

2024

Graph Contrastive Invariant Learning from the Causal Perspective

AAAI 2024technical

Graph contrastive learning (GCL), learning the node representation by contrasting two augmented graphs in a self-supervised way, has attracted considerable attention. GCL is usually believed to learn the invariant representation. However, does this understanding always hold in practice? In this pape…

2022

Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure

NeurIPS 2022accept

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 investigation on the training graphs with severe bias, surprisingly, we discover that GNNs always tend to explore the s…