通过分层注意力与相关性剪枝，提升异构图神经网络的可解释性与效率。
Hierarchical Attention-based Graph Neural Network with Relevance-driven Pruning
- 分层注意力机制分离传感器与上下文计算，支持16类节点、18类边
- 剪枝后边数减少27%，分类准确率反而提升2.46%~4.1%
- 适用于需要高可解释性与低延迟的工业级图分析场景
图神经网络在关系推理中表现优异，但面临异构节点类型可解释性不足和大规模噪声图上消息传递计算开销大的问题。本文提出分层注意力异构图神经网络（HA-HeteroGNN），通过统一的可解释性到剪枝流程解决上述问题。采用两级注意力机制，在16类节点和18类边之间分离传感器级与上下文级计算，基于注意力机制生成节点相关性得分，无需梯度反向传播。这些得分作为剪枝依据：移除持续不具信息量的节点后，图边数减少27%，同时所有模型变体的分类准确率提升2.46%~4.1%，挑战了剪枝必然牺牲精度的旧有假设。在包含5万条记录、覆盖11个报告类别的合成数据集上，跨策略解释稳定性达97.5%，传感器归因具领域一致性，训练时间最多降低43.9%，单样本实时推理延迟约5860毫秒。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) excel at relational reasoning but face two persistent challenges: the lack of interpretable attribution for heterogeneous node types, and the computational overhead of message passing over large, noisy graphs. We propose the Hierarchical Attention-based Heterogeneous GNN (HA-HeteroGNN), a framework that addresses both issues through a unied explainability-to-pruning pipeline. A two-tier attention mechanism separates sensor-level and context-level computation across 16 node types and 18 edge types, producing per-node relevance scores via an attention-based GNN Explainer without requiring gradient backpropagation. These relevance scores then serve as a principled pruning criterion: removing nodes identied as consistently uninformative yields a 27% reduction in graph edges while simultaneously improving classication accuracy by 2.46.1% across all model variants, challenging the conventional assumption that pruning necessarily trades accuracy for eciency. Experiments on a 50,000-record synthetic dataset spanning 11 report categories demonstrate 97.5% cross-strategy explanation stability and domain consistent sensor attribution, with training-time reductions of up to 43.9% and real-time inference latency of approximately 5860 ms per sample.
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