用编程语言生成可解释的图表示，提升分类准确率与可解释性
GDLNN: Marriage of Programming Language and Neural Networks for Accurate and Easy-to-Explain Graph Classification
- 通过专用编程语言GDL构建可解释的图表示层
- 在多数基准数据集上超越主流GNN方法，精度更高
- 解释成本低，适合需要透明决策的场景
我们提出GDLNN，一种用于图分类的新机器学习架构。GDLNN结合了领域特定编程语言GDL与神经网络。其核心优势在于GDL层，能够生成表达性强且可解释的图表示。由于表示具有可解释性，现有模型解释技术可直接用于解析GDLNN的预测结果。实验表明，基于GDL的表示在多数图分类基准数据集上表现优异，优于主流图学习方法如GNN。同时，应用已有解释技术可获得高质量预测解释。此外，在包含解释成本的情况下，GDLNN整体开销较低。
原文摘要 · Abstract (English)
We present GDLNN, a new graph machine learning architecture, for graph classification tasks. GDLNN combines a domain-specific programming language, called GDL, with neural networks. The main strength of GDLNN lies in its GDL layer, which generates expressive and interpretable graph representations. Since the graph representation is interpretable, existing model explanation techniques can be directly applied to explain GDLNN's predictions. Our evaluation shows that the GDL-based representation achieves high accuracy on most graph classification benchmark datasets, outperforming dominant graph learning methods such as GNNs. Applying an existing model explanation technique also yields high-quality explanations of GDLNN's predictions. Furthermore, the cost of GDLNN is low when the explanation cost is included.
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