一种面向图神经网络的节点特征升维分析方法  

A Node Feature Dimension Augmentation Analysis Method for Graph Neural Networks

作　　者：潘永昊 张苒苒 于洪涛 黄瑞阳[1] PAN Yonghao;ZHANG Ranran;YU Hongtao;HUANG Ruiyang(Information Engineering University,Zhengzhou 450001,China;Unit 31015,Beijing 100840,China)

机构地区：[1]信息工程大学,河南郑州450001 [2]31015部队,北京100840

出　　处：《信息工程大学学报》2025年第2期203-208,共6页Journal of Information Engineering University

基　　金：嵩山实验室项目(纳入河南省重大科技专项)(221100210700-3)。

摘　　要：现有针对图神经网络节点特征重要性的研究方法主要基于图结构特征,难以针对每个节点特征的重要性进行分析。为解决这一问题,提出一种面向图神经网络的节点特征升维分析方法。首先,在高维空间中对节点特征进行升维表示,并针对高维节点特征数据稀疏的特点构建适配数据结构;然后,扩展定义高维空间图神经网络计算规则并对高维节点特征进行计算;最后,对高维空间中的计算结果分析得出各个节点特征在图神经网络计算结果中的权重。实验中,大权重节点特征对模型准确率影响最大为62.88%,验证了该权重能够有效反应输入节点特征的重要性。Existing research methods for analyzing the importance of node features in graph neural networks are primarily based on structural features,making the analysis of each node feature’s importance difficult.To address this issue,a node feature dimension augmentation analysis method for graph neural networks is proposed.Firstly,node features are represented in a high-dimensional space,and a data structure adapted for the sparse characteristics of high-dimensional node feature data is constructed.Then,the computational rules of graph neural networks are extended and defined in high-dimensional space,with high-dimensional node features being calculated accordingly.Finally,the calculation results in high-dimensional space are analyzed,and the weights of each node feature in the graph neural network calculation results are obtained.In experiments,it is found that node features with large weights have the greatest impact on model accuracy,accounting for 62.88%,which verifies that the weights can effectively reflect the importance of input node features.

关 键 词：图神经网络 节点特征 升维 权重分析 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]