破解GNN在异质节点上失效的根源：不是稀有性，而是信息利用不足。
Disentangling Homophily and Rarity: Explaining Failure in Graph Neural Networks
- 通过重训分类头即可提升异质节点分类性能
- 稀有但同质的节点仍易被正确分类
- 适合研究GNN可解释性与鲁棒性的学者
图神经网络在分类异质节点时表现不佳，学界普遍认为是因这些节点属于少数群体或邻居聚合机制导致。本文通过在五个不同同质性水平的数据集上评估六种GNN模型，发现即使异质节点稀少，只要其同质性高，仍能被有效分类，挑战了少数群体泛化视角。同时发现，正确分类异质节点所需信息往往可通过重新训练模型的分类头（尤其是最后线性层）恢复，表明问题主要在于信息利用而非结构缺陷。
原文摘要 · Abstract (English)
Are heterophilic nodes in a graph harder to classify because they are heterophilic or because they are rare? Some existing work frames classification of such nodes as a subgroup generalisation problem, where a model performs well on the majority group at the expense of the rare group. Others explain this as a problem of neighbourhood aggregation in graph neural networks (GNNs). We assess these two viewpoints through a detailed evaluation of six GNNs on five datasets of varying homophily, and find that homophilic nodes tend to be easier to classify, even when they are rare---challenging the subgroup framing. However, our findings also nuance existing beliefs about how GNNs misrepresent heterophilic nodes. We demonstrate that the information needed to classify heterophilic nodes correctly is often recoverable by retraining the classification head of a model, or even just the final linear classification layer.
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