  Graph Neural Networks (GNNs) have emerged as a powerful tool for
semi-supervised node classification tasks. However, recent studies have
revealed various biases in GNNs stemming from both node features and graph
topology. In this work, we uncover a new bias - label position bias, which
indicates that the node closer to the labeled nodes tends to perform better. We
introduce a new metric, the Label Proximity Score, to quantify this bias, and
find that it is closely related to performance disparities. To address the
label position bias, we propose a novel optimization framework for learning a
label position unbiased graph structure, which can be applied to existing GNNs.
Extensive experiments demonstrate that our proposed method not only outperforms
backbone methods but also significantly mitigates the issue of label position
bias in GNNs.
