Debiased Graph Neural Networks with Agnostic Label Selection Bias. (arXiv:2201.07708v1 [cs.LG])

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 …

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