HSEGRL:一种分层可自解释的图表示学习模型
HSEGRL:A Hierarchical Self-Explainable Graph Representation Learning Model
李平 1宋舒寒 1张园 1曹华伟 2叶笑春 3唐志敏3
作者信息
- 1. 处理器芯片全国重点实验室(中国科学院计算技术研究所) 北京 100190;中国科学院大学 北京 100049
- 2. 处理器芯片全国重点实验室(中国科学院计算技术研究所) 北京 100190;中国科学院大学南京学院 南京 211135
- 3. 处理器芯片全国重点实验室(中国科学院计算技术研究所) 北京 100190
- 折叠
摘要
近年来,随着图神经网络(graph neural network,GNN)技术在社交、信息、化学、生物等领域的广泛应用,GNN可解释性也受到广泛的关注.然而,现有的解释方法无法捕获层次化的解释信息,同时,这些层次信息未能被充分利用以提升图分类任务的准确率.基于这一问题,提出了一种层次化自解释的图表示学习(hierarchical self-explanation graph representation learning,HSEGRL)模型,该模型通过发现图结构中的层次信息进行图分类预测的同时,输出层次化的模型自解释结果.具体而言,针对图层次信息的发现设计了提取信息的基本单元——解释子,该解释子由提取节点特征的编码器获取层次化解释感知子图的池化层和抽取高阶解释信息的解码器组成.其中,为了准确提取层次化的解释子图,针对该模型的池化操作进行了解释感知优化设计,该设计通过评估模型的拓扑及特征重要性,层次化地筛选解释子图,实现分层自解释的同时完成图分类任务.HSEGRL是一个功能完备且便于迁移的图表示学习自解释模型,可以层次化综合考虑模型的拓扑信息与节点特征信息.在模型有效性验证层面,分别在分子、蛋白质和社交数据集上进行大量实验,实验结果表明所提模型在图分类任务中的分类准确率高于已有的先进的GNN自解释模型和GNN模型,并通过可视化分层解释结果的信息证明了该解释方法可信.
Abstract
In recent years,with the extensive application of graph neural network(GNN)technology in the fields such as social network,information,chemistry and biology,the interpretability of GNN has attracted widespread attention.However,prevailing explanation methods fail to capture the hierarchical explanation information,and these hierarchical information has not been fully utilized to improve the classification accuracy of graph tasks.To address this issue,we propose a hierarchical self-explanation graph representation learning model called HSEGRL(hierarchical self-explanation graph representation learning).This model,by discovering hierarchical information in the graph structure,predicts graph classifications while outputting hierarchical self-explanation results.Specifically,we design the basic unit for extracting hierarchical information—interpreters.These interpreters consist of an encoder that extracts node features,a pooling layer that perceives hierarchical explanation-aware subgraphs,and a decoder that refines higher-order explanation information.We refine the pooling mechanism with an explanation-aware strategy,enabling the hierarchical selection of subgraphs based on the evaluation of the model's topology and feature importance,thereby facilitating hierarchical self-explanation in conjunction with graph classification.HSEGRL is a functionally comprehensive and transferable self-explanatory graph representation learning framework that can hierarchically consider the model's topological information and node feature information.We conduct extensive experiments on datasets from molecular,protein,and social network,and demonstrate that HSEGRL surpasses existing advanced self-explanatory graph neural network models and graph neural network models in terms of graph classification performance.Furthermore,the visualization of layered explanation outcomes substantiates the credibility of our proposed explanation methodology.
关键词
图表示学习/图神经网络/可自解释模型/图拓扑/消息传递机制Key words
graph representation learning/graph neural networks/self-explainable model/graph topology/message passing mechanism引用本文复制引用
基金项目
国家重点研发计划项目(2023YFB4502305)
北京市自然科学基金项目(4232036)
CAS青年创新促进会项目()
出版年
2024
NETL
NSTL
万方数据