Adaptive Node Feature Selection For Graph Neural Networks
This addresses the need for interpretable and efficient GNNs in domains like social networks or bioinformatics, though it is incremental as it builds on existing feature selection ideas adapted to graph-structured data.
The paper tackles the problem of identifying and removing unnecessary node features in graph neural networks (GNNs) to improve interpretability, reduce dimensionality, and potentially enhance performance, by proposing an adaptive feature selection method based on validation performance changes during training, with empirical results demonstrating its flexibility across different graph architectures and challenging settings.
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, reducing dimensionality, and even improving performance by eliminating unhelpful variables. However, graph-structured data introduces complex dependencies that may not be amenable to classical feature importance metrics. Inspired by this challenge, we present a 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 intervention-based approach by characterizing how GNN performance depends on the relationships between node data and graph structure. Not only do we return feature importance scores once training concludes, we also track how relevance evolves as features are successively dropped. We can therefore monitor if features are eliminated effectively and also evaluate other metrics with this technique. Our empirical results verify the flexibility of our approach to different graph architectures as well as its adaptability to more challenging graph learning settings.