GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks

Peter Müller, Lukas Faber, Karolis Martinkus, Roger Wattenhofer

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Keywords: Graph Neural Networks, GNN, Explainability, Decision Trees

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TL;DR: GraphChef integrate Decision Trees into Graph Neural Networks to allow explaining the full decision process.

Abstract: We propose a new self-explainable Graph Neural Network (GNN) model: GraphChef. GraphChef integrates decision trees into the GNN message passing framework. Given a dataset, GraphChef returns a set of rules (a recipe) that explains each class in the dataset unlike existing GNNs and explanation methods that reason on individual graphs. Thanks to the decision trees, GraphChef recipes are human understandable. We also present a new pruning method to produce small and easy to digest trees. Experiments demonstrate that GraphChef reaches comparable accuracy to not self-explainable GNNs and produced decision trees are indeed small. We further validate the correctness of the discovered recipes on datasets where explanation ground truth is available: Reddit-Binary, MUTAG, BA-2Motifs, BA-Shapes, Tree-Cycle, and Tree-Grid.

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Primary Area: visualization or interpretation of learned representations

Submission Number: 8971