页面:基于原型的模型级解释图形神经网络
概括
我们介绍了基于原型的GNN解释器 (PGE),这是一种用于图形神经网络 (GNN) 的新型模型级解释方法. PGE发现了人类可解释的原型图形,以解释GNN对图形分类的学习.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形表示学习学习学习图形表示学习
背景情况:
- 图形神经网络 (GNN) 是强大的图形表示学习,但需要可解释性.
- 现有的GNN解释方法主要集中在实例级解释上.
- 越来越需要模型级的解释来揭示GNN整体学习的内容.
研究的目的:
- 提出基于原型的GNN解释器 (PGE),一种新的模型级GNN解释方法.
- 为生成人类可解释的原型图形,解释GNN学习用于图形分类.
- 与实例级方法相比,提供更简洁,更全面的解释.
主要方法:
- PGE集群类歧视性输入图形嵌入,以选择代表性的图形.
- 它通过节点嵌入和原型评分函数代地搜索高匹配节点组.
- 该方法发现了常见的子图模式,产生了一个原型图作为解释.
主要成果:
- 在六个图形分类数据集上,PGE在质量和数量上优于最先进的模型级解释方法.
- 实验研究表明PGE与实例级方法的关系.
- 展示了原型评分函数在数据稀缺环境中的稳定性和计算效率.
结论:
- PGE为模型级GNN解释提供了一种新且有效的方法.
- 发现的原型图表为GNN决策提供了人类可解读的见解.
- PGE在基于图形的机器学习中推进了可解释AI领域.
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