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. In reality, the test data is not even available
during the training process, making selection bias agnostic. Training GNNs with
biased selected nodes leads to significant parameter estimation bias and
greatly impacts the generalization ability on test nodes. In this paper, we
first present an experimental investigation, which clearly shows that the
selection bias drastically hinders the generalization ability of GNNs, and
theoretically prove that the selection bias will cause the biased estimation on
GNN parameters. Then to remove the bias in GNN estimation, we propose a novel
Debiased Graph Neural Networks (DGNN) with a differentiated decorrelation
regularizer. The differentiated decorrelation regularizer estimates a sample
weight for each labeled node such that the spurious correlation of learned
embeddings could be eliminated. We analyze the regularizer in causal view and
it motivates us to differentiate the weights of the variables based on their
contribution on the confounding bias. Then, these sample weights are used for
reweighting GNNs to eliminate the estimation bias, thus help to improve the
stability of prediction on unknown test nodes. Comprehensive experiments are
conducted on several challenging graph datasets with two kinds of label
selection biases. The results well verify that our proposed model outperforms
the state-of-the-art methods and DGNN is a flexible framework to enhance
existing GNNs.

本文提出用于消除图神经网络中选择偏差的 Debiased Graph Neural Networks 方法，并通过多个实验验证其超越了现有方法，DGNN 是增强现有 GNN 的灵活框架。