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arXiv · 2410.07764

Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts

Shiye Su·Iulia Duta·Lucie Charlotte Magister·Pietro Liò
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Abstract

Hypergraph neural networks are a class of powerful models that leverage the message passing paradigm to learn over hypergraphs, a generalization of graphs well-suited to describing relational data with higher-order interactions. However, such models are not naturally interpretable, and their explainability has received very limited attention. We introduce SHypX, the first model-agnostic post-hoc explainer for hypergraph neural networks that provides both local and global explanations. At the instance-level, it performs input attribution by discretely sampling explanation subhypergraphs optimized to be faithful and concise. At the model-level, it produces global explanation subhypergraphs using unsupervised concept extraction. Extensive experiments across four real-world and four novel, synthetic hypergraph datasets demonstrate that our method finds high-quality explanations which can target a user-specified balance between faithfulness and concision, improving over baselines by 25 percent points in fidelity on average.

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Shiye Su, Iulia Duta, Lucie Charlotte Magister, Pietro Liò. 2024-10-10. Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts. https://arxiv.org/abs/2410.07764

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