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

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 manages to provide accurate and human-interpretable global explanations in both synthetic and real world datasets.

Type Of Submission: Extended abstract (max 4 main pages).

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Type Of Submission: Extended abstract.

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