Learning to Rank Features to Enhance Graph Neural Networks for Graph Classification

A common strategy to enhance the predictive performance of graph neural networks ({GNNs}) for graph classification is to extend input graphs with node- and graph-level features. However, identifying the optimal feature set for a specific learning task remains a significant challenge, often requiring domain-specific expertise. To address this, we propose a general two-step method that automatically selects a compact, informative subset from a large pool of candidate features to improve classification accuracy. In the first step, a {GNN} is trained to estimate the importance of each feature for a given graph. In the second step, the model generates feature rankings for the training graphs, which are then aggregated into a global ranking. A top-ranked subset is selected from this global ranking and used to train a downstream graph classification {GNN}. Experiments on real-world and synthetic datasets show that our method outperforms various baselines, including models using all candidate features, and achieves state-of-the-art results on several benchmarks.