xAI-Drop: Don't Use What You Cannot Explain
This addresses the challenge of making GNNs more reliable for critical applications like social network analysis and bioinformatics, though it is an incremental improvement over existing dropping methods.
The paper tackles the problem of poor generalization and interpretability in Graph Neural Networks (GNNs) by introducing xAI-Drop, a dropping regularizer that uses explainability to exclude noisy elements, resulting in improved accuracy and explanation quality on real-world datasets.
Graph Neural Networks (GNNs) have emerged as the predominant paradigm for learning from graph-structured data, offering a wide range of applications from social network analysis to bioinformatics. Despite their versatility, GNNs face challenges such as lack of generalization and poor interpretability, which hinder their wider adoption and reliability in critical applications. Dropping has emerged as an effective paradigm for improving the generalization capabilities of GNNs. However, existing approaches often rely on random or heuristic-based selection criteria, lacking a principled method to identify and exclude nodes that contribute to noise and over-complexity in the model. In this work, we argue that explainability should be a key indicator of a model's quality throughout its training phase. To this end, we introduce xAI-Drop, a novel topological-level dropping regularizer that leverages explainability to pinpoint noisy network elements to be excluded from the GNN propagation mechanism. An empirical evaluation on diverse real-world datasets demonstrates that our method outperforms current state-of-the-art dropping approaches in accuracy, and improves explanation quality.