让图神经网络自动筛选关键特征，提升性能并解释决策。
Adaptive Node Feature Selection For Graph Neural Networks
- 训练中通过打乱特征值评估其对验证效果的影响，动态识别重要特征。
- 无需先验假设，性能媲美定制化特征选择方法，且提前给出有效评分。
- 适用于多种图学习任务，适合需要可解释性和降维的场景。
我们提出一种图神经网络（GNN）的自适应节点特征选择方法，在训练过程中识别并移除冗余特征。衡量特征对模型输出的贡献是解释决策和降低维度的关键，但图结构数据中的复杂依赖关系使得经典特征重要性度量不适用。受此启发，我们提出一种数据、模型和任务无关的方法，基于打乱特征值后验证性能的变化来确定相关特征。理论层面，我们分析了节点数据与图结构间关系对GNN性能的影响。实验表明：(i) 该方法在通用性上优于依赖先验假设的定制化特征选择；(ii) 能在模型完全训练前就生成有意义的特征重要性评分；(iii) 所得评分能有效揭示不同图学习设置下的相关属性。
原文摘要 · Abstract (English)
We propose an adaptive node feature selection approach for graph neural networks (GNNs) that identifies and removes unnecessary features during training. The ability to measure how features contribute to model output is key for interpreting decisions and reducing dimensionality by eliminating unhelpful variables. However, graph-structured data introduces complex dependencies that may be unsuited to classical feature importance metrics. Inspired by this, we present a data-, model-, and task-agnostic method that determines relevant features during training based on changes in validation performance upon permuting feature values. We theoretically motivate our approach by characterizing how the relationships between node data and graph structure influences GNN performance. Empirically, we show that (i) our highly general approach rivals the performance of tailored feature selection approaches that exploit prior assumptions; (ii) we return meaningful feature importance scores well before the GNN is fully trained; and (iii) our scores demonstrably extract relevant properties that inform feature importance for various graph learning settings.
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