Global Explainability of GNNs via Logic Combination of Learned Concepts

Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Lio, Andrea Passerini

Keywords: Explainability, Graph Neural Networks, Concept Learning

TL;DR: We propose GLGExplainer, the first Global Explainer for GNNs capable of generating explanations as arbitrary Boolean combinations of graphical concepts.

Abstract:

While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less explored, despite its potential in interpretability and debugging. Existing solutions either simply list local explanations for a given class, or generate a synthetic prototypical graph with maximal score for a given class, completely missing any combinatorial aspect that the GNN could have learned.
In this work, we propose GLGExplainer (Global Logic-based GNN Explainer), the first Global Explainer capable of generating explanations as arbitrary Boolean combinations of learned graphical concepts. GLGExplainer is a fully differentiable architecture that takes local explanations as inputs and combines them into a logic formula over graphical concepts, represented as clusters of local explanations.
Contrary to existing solutions, GLGExplainer provides accurate and human-interpretable global explanations that are perfectly aligned with ground-truth explanations (on synthetic data) or match existing domain knowledge (on real-world data). Extracted formulas are faithful to the model predictions, to the point of providing insights into some occasionally incorrect rules learned by the model, making GLGExplainer a promising diagnostic tool for learned GNNs.

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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2210.07147/code)