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
spurious correlations to make decision, even if the causal correlation always
exists. This implies that existing GNNs trained on such biased datasets will
suffer from poor generalization capability. By analyzing this problem in a
causal view, we find that disentangling and decorrelating the causal and bias
latent variables from the biased graphs are both crucial for debiasing.
Inspiring by this, we propose a general disentangled GNN framework to learn the
causal substructure and bias substructure, respectively. Particularly, we
design a parameterized edge mask generator to explicitly split the input graph
into causal and bias subgraphs. Then two GNN modules supervised by
causal/bias-aware loss functions respectively are trained to encode causal and
bias subgraphs into their corresponding representations. With the disentangled
representations, we synthesize the counterfactual unbiased training samples to
further decorrelate causal and bias variables. Moreover, to better benchmark
the severe bias problem, we construct three new graph datasets, which have
controllable bias degrees and are easier to visualize and explain. Experimental
results well demonstrate that our approach achieves superior generalization
performance over existing baselines. Furthermore, owing to the learned edge
mask, the proposed model has appealing interpretability and transferability.
Code and data are available at: this https URL.

本文研究了图神经网络的偏差问题及其对泛化性能的影响，并提出了一种能够将因果关系和偏差变量进行分离的图神经网络框架，其中利用参数化边缘生成器将输入图形分为因果图和偏差子图并分别训练两个 GNN 模块，实现了对原模型的改进并取得了超越基线模型的泛化效果。