Automated message selection for robust Heterogeneous Graph Contrastive Learning

Abstract

Heterogeneous Graph Contrastive Learning (HGCL) has attracted lots of attentions because of eliminating the requirement of node labels. The encoders used in HGCL mainly are message-passing based Heterogeneous Graph Neural Networks, which are vulnerable to edge perturbations. Recently, a few HGCL models replace the polluted graph with KNN graph or threshold graph, which both have flaws: (1) each node in KNN graph have the same degree K, it is irrational and loses original structural features; (2) the threshold is selected artificially, which hinders both effectiveness and interpretability. To tackle the above issues, we propose an Automated Message Selection based Heterogeneous Graph Contrastive Learning (AMS-HGCL) model. We first set relation view and meta-path view for contrast. Then, a robust encoder is proposed to defend structural attacks for both views by automatically selecting harmless messages, without setting all nodes having the same number of neighbors. The learned probabilities of messages can show harmful features directly, which makes our model interpretable. Finally, we design a novel cross-view contrastive loss to optimize AMS-HGCL and output robust node representations. The experimental results on four real datasets demonstrate that AMS-HGCL is feasible and effective.