优化关系图结构可显著提升深度学习在数据库上的表现
What Makes a Desired Graph for Relational Deep Learning?
- 通过过滤与注入双重操作，自动调整关系图结构
- 在26个任务中准确率提升，推理成本常降低
- 适合做数据库驱动的图神经网络研究者
关系深度学习（RDL）将关系型数据库（RDBs）转换为异构图，但直接从数据库模式生成的图往往不适用于图神经网络（GNN）的关系推理。我们研究了何种图结构适合深度学习，发现模式导出的图存在信息过载和语义碎片化两大系统性缺陷。实证分析表明，理想的图并非原始模式，而是经过受控结构适配的结果。性能取决于平衡两个操作：通过过滤缓解信息过载，通过注入修复语义碎片。具体而言，过滤具有偏差-方差调节作用，效果非单调；而注入仅在显式恢复原模式缺失的关系依赖时才有效。基于此，我们提出一个端到端结构优化器，自动应用上述两种操作。在涵盖分类、回归与推荐的26个任务中，优化后的图持续提升准确率，且常降低推理开销。
原文摘要 · Abstract (English)
Relational deep learning (RDL) converts relational databases (RDBs) into heterogeneous graphs, but graphs derived directly from database schemas are often not well suited for how graph neural networks (GNNs) perform relational reasoning. We study what makes a relational graph suitable for deep learning and show that schema-derived graphs suffer from two systematic failures: information overload and semantic fragmentation. Our empirical analysis reveals that the desired graph is not the raw schema, but a result of controlled structural adaptation. Performance depends on balancing two operations: mitigating information overload via filtering, and repairing semantic fragmentation via injection. Specifically, filtering serves as a bias-variance knob with non-monotonic effects, while injection improves performance only when it explicitly restores the relational dependencies missing from the original schema. Based on these findings, we develop an end-to-end structural optimizer that applies both operations to adapt relational graphs automatically. Across 26 tasks spanning classification, regression, and recommendation, the optimized graphs consistently improve accuracy while often reducing inference cost.
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