HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networks

• URL: http://arxiv.org/abs/2606.03495v1
• Date: Tue, 02 Jun 2026 11:12:01 GMT
• Title: HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networks
• Abstract summary: Heterogeneous graph neural networks (HGNNs) have demonstrated remarkable performance in modeling complex data.<n>Existing explanation methods suffer from two major limitations.<n>We propose HiSE, a lightweight feature-oriented interpretable model for relational HGNNs.
• Score: 15.506128015180472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heterogeneous graph neural networks (HGNNs) have demonstrated remarkable performance in modeling complex relational data, however their interpretability in high-stakes applications remains a critical challenge. Existing explanation methods suffer from two major limitations: on the one hand, the generated explanations fail to reflect the inherent semantic hierarchy of HGNNs, resulting in a lack of fidelity to the model's internal decision-making mechanism; on the other hand, feature explanations often rely on complex search or perturbation mechanisms, leading to excessive computational complexity and poor efficiency. To address these issues, we propose HiSE, a lightweight feature-oriented interpretable model for HGNNs. HiSE achieves semantically aware feature explanations through hierarchical semantic modeling: at the semantic level, local surrogate models based on the Least Absolute Shrinkage and Selection Operator (LASSO) are employed to learn sparse feature representations under each semantic view; at the cross-semantic level, the contributions of different semantic views are adaptively characterized via KL divergence to produce a unified explanation. Extensive experiments demonstrate that HiSE outperforms existing methods in terms of fidelity, robustness, and cross-semantic explanation capability, while its lightweight framework incurs low computational overhead, enabling efficient application to large-scale, complex real-world heterogeneous graphs.

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