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Global Explainability of GNNs via Logic Combination of Learned Concepts

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While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behavior 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 GLG Explainer (Global Logic-based GNN Explainer), the first Global Explainer capable of generating explanations as arbitrary Boolean combinations of learned graphical concepts. GLG Explainer 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, GLG Explainer provides accurate and human-interpretable global explanations that are 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 GLG Explainer a promising diagnostic tool for learned GNNs.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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