PL4XGL: A Programming Language Approach to Explainable Graph Learning

Abstract

In this article, we present a new, language-based approach to explainable graph learning. Though graph neural networks (GNNs) have shown impressive performance in various graph learning tasks, they have severe limitations in explainability, hindering their use in decision-critical applications. To address these limitations, several GNN explanation techniques have been proposed using a post-hoc explanation approach providing subgraphs as explanations for classification results. Unfortunately, however, they have two fundamental drawbacks in terms of additional explanation costs and 2) the correctness of the explanations. This paper aims to address these problems by developing a new graph-learning method based on programming language techniques. Our key idea is two-fold: 1) designing a graph description language (GDL) to explain the classification results and 2) developing a new GDL-based interpretable classification model instead of GNN-based models. Our graph-learning model, called PL4XGL, consists of a set of candidate GDL programs with labels and quality scores. For a given graph component, it searches the best GDL program describing the component and provides the corresponding label as the classification result and the program as the explanation. In our approach, learning from data is formulated as a program-synthesis problem, and we present top-down and bottom-up algorithms for synthesizing GDL programs from training data. Evaluation using widely-used datasets demonstrates that PL4XGL produces high-quality explanations that outperform those produced by the state-of-the-art GNN explanation technique, SubgraphX. We also show that PL4XGL achieves competitive classification accuracy comparable to popular GNN models.