Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts

ICLR 2025 Conference Submission13024 Authors

Keywords: graph neural nets, hypergraph neural nets, explainability, concepts

TL;DR: We develop a model-agnostic post hoc explainer for hypergraph neural networks.

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.

Primary Area: interpretability and explainable AI

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Submission Number: 13024