提出可自解释的异构图神经网络，能自动发现关系数据中的关键路径。
A Self-Explainable Heterogeneous GNN for Relational Deep Learning
- 基于多出现聚合信息学习关系路径，不依赖专家标注。
- 在合成与真实数据上显著优于现有方法。
- 适合需要可解释性的关系数据分析场景。
近年来，人们越来越关注将关系型数据库视为异构图，以应用图神经网络（GNN）进行预测任务。然而，现有GNN方法难以应对由大量表和关系构成的异构图复杂性。传统方法要么考虑所有可能的关系元路径，导致无法扩展；要么依赖领域专家识别相关元路径。最近的方法虽能在无监督下学习有信息量的元路径，但假设节点类别仅取决于元路径是否存在。本文提出一种自解释的异构图神经网络，支持节点类别依赖于多个元路径出现的聚合信息。实验表明，在关系数据库场景下，该方法能有效识别有信息量的元路径，准确捕捉模型推理机制，在合成与真实数据集上均显著优于现有方法。
原文摘要 · Abstract (English)
Recently, significant attention has been given to the idea of viewing relational databases as heterogeneous graphs, enabling the application of graph neural network (GNN) technology for predictive tasks. However, existing GNN methods struggle with the complexity of the heterogeneous graphs induced by databases with numerous tables and relations. Traditional approaches either consider all possible relational meta-paths, thus failing to scale with the number of relations, or rely on domain experts to identify relevant meta-paths. A recent solution does manage to learn informative meta-paths without expert supervision, but assumes that a node's class depends solely on the existence of a meta-path occurrence. In this work, we present a self-explainable heterogeneous GNN for relational data, that supports models in which class membership depends on aggregate information obtained from multiple occurrences of a meta-path. Experimental results show that in the context of relational databases, our approach effectively identifies informative meta-paths that faithfully capture the model's reasoning mechanisms. It significantly outperforms existing methods in both synthetic and real-world scenario.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。