让图神经网络的复杂性显式体现在数据中，提升可解释性。
From Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning
- 将模型复杂度迁移到数据空间，用增强图结构实现透明学习
- 简单学生模型可复现复杂教师模型性能，保持精度不变
- 适合关注GNN可解释性与架构设计的研究者
图神经网络（GNNs）虽性能优异，但对人类而言往往不透明，难以理解不同架构之间的差异。现有可解释性方法仅能解释单个预测结果，无法揭示模型间性能差距的根本原因。为此，本文提出模型到数据（M2D）蒸馏框架，通过将教师模型的复杂性迁移到数据空间，构建特征和结构更丰富的增强图，使简单的学生模型也能达到教师模型的性能。该方法将模型行为显式化于数据中，便于人类直接观察架构优势。实验表明，M2D可清晰揭示公平性目标与基于注意力的聚合机制等内在原理，在保留性能的同时显著提升GNN透明度。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) achieve high performance but can be opaque to humans, making it difficult to understand and compare the many proposed architectures. While existing explainability methods attribute individual predictions to nodes, edges, or features, they do not provide architectural transparency or explain the fundamental performance gap between simple and more complex models. To address this limitation, we introduce Model-to-Data (M2D) distillation, a new framework that increases transparency by transferring model complexity into the data space. M2D distills the teacher model into an augmented graph with enriched features and structure, enabling a simple student to match the teacher's performance. By materializing model behavior in the data, our approach allows humans to inspect architectural advantages directly. We show that M2D reveals underlying mechanisms such as fairness objectives and attention-based aggregation in an interpretable way, enhancing GNN transparency while preserving performance.
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