Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts
<|reference_start|>Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts: 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.<|reference_end|>
@article{su2024explaining,
    title={Explaining Hypergraph Neural Networks: From Local Explanations to Global
  Concepts},
    author={Shiye Su, Iulia Duta, Lucie Charlotte Magister, Pietro Li`o},
    journal={arXiv preprint arXiv:2410.07764},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07764},
    primaryClass={cs.LG}
}