BA-GNN: On Learning Bias-Aware Graph Neural Network

Graph Neural Networks (GNNs) show promising results for semi-supervised learning tasks on graphs, which become favorable comparing with other approaches. However, similar to other machine learning models, GNNs might suffer from the bias issue because of the distribution shift between training and testing node distributions. More importantly, the test node distribution in the graph is generally unknown during model training in practice. In this paper, we focus on how to address the bias issue on graphs and learn a graph neural network model that is robust to arbitrary unknown distribution shifts. To address this problem, we propose a novel Bias-Aware Graph Neural Network (BA-GNN) framework by learning node representations that are invariant across different distributions for invariant prediction. Specifically, our BA-GNN framework contains two interactive parts, one for bias identification and the other for invariant prediction. To learn invariant feature and aggregated representation, our BA-GNN learns multiple biased graph partitions and selects feature, neighbor, and propagation steps for nodes under multiple biased graph partitions. Extensive experiments show that our proposed BA-GNN framework can significantly improve different GNNs backbones such as GCN, GAT, APPNP and GraphSAGE on different datasets.