HiSE：一种用于异构图神经网络的轻量级层次语义解释器
HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networks
AI总结：
提出HiSE，一种轻量级特征导向的可解释模型，通过层次语义建模（语义级LASSO稀疏特征学习和跨语义级KL散度自适应融合）实现高保真、低计算开销的异构图神经网络解释。
摘要：
异构图神经网络（HGNNs）在建模复杂关系数据方面表现出色，然而在高风险应用中的可解释性仍然是一个关键挑战。现有的解释方法存在两个主要局限性：一方面，生成的解释未能反映HGNNs固有的语义层次，导致对模型内部决策机制的保真度不足；另一方面，特征解释通常依赖于复杂的搜索或扰动机制，导致计算复杂度过高且效率低下。为了解决这些问题，我们提出了HiSE，一种轻量级特征导向的HGNNs可解释模型。HiSE通过层次语义建模实现语义感知的特征解释：在语义层面，采用基于最小绝对收缩和选择算子（LASSO）的局部代理模型学习每个语义视图下的稀疏特征表示；在跨语义层面，通过KL散度自适应地表征不同语义视图的贡献，生成统一的解释。大量实验表明，HiSE在保真度、鲁棒性和跨语义解释能力方面优于现有方法，同时其轻量级框架具有较低的计算开销，能够高效应用于大规模、复杂的真实世界异构图。
英文摘要：
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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