计算机工程2025，Vol.51Issue(6)：38-48,11.DOI:10.19678/j.issn.1000-3428.0068976
基于个性化PageRank高阶邻域聚合的图神经网络增强
Graph Neural Network Enhancement Based on Personalized PageRank Higher Order Neighborhood Aggregation
摘要
Abstract
The key idea behind Graph Neural Network(GNN)is to learn the information representation of a target node by aggregating neighborhood information through the topology of a graph;however,edges that are not relevant to a downstream task or nodes with limited neighbors may limit the representation of the neural network.Existing enhancement methods seldom focus on both structure and features simultaneously when enhancing graph data.Among them,existing local area enhancement methods use generative models to generate features through first-order neighborhoods and cannot obtain more relevant higher-order neighborhood information for nodes.To address this phenomenon,this study presents an effective data enhancement strategy.First,an edge prediction model is used to adjust the topology of a graph to improve the Signal-to-Noise Ratio(SNR)and facilitate the message transfer between nodes.Second,a Personalized PageRank(PPR)algorithm is used to aggregate the effective information in multiorder neighborhoods from a global perspective for global feature enhancement.Finally,the generative model is used to generate more features for local enhancement,which enriches node expression,especially for low-degree nodes.Experiments show that the accuracies of Graph Convolutional Network(GCN)and Graph Attention Network(GAT)models are improved by 3.1 and 1.3 percentage points on average,respectively,on the Cora,CiteSeer,and PubMed datasets with this data enhancement strategy.This result shows that performance improves to an extent when this strategy is applied to neural network architectures with different benchmark sets.关键词
数据增强/个性化PageRank/生成模型/神经网络/全局聚合/多阶邻域Key words
data augmentation/Personalized PageRank(PPR)/generative model/neural network/global aggregation/multi-order neighborhood分类
计算机与自动化引用本文复制引用
商雅名,吴安彪,袁野,王一舒..基于个性化PageRank高阶邻域聚合的图神经网络增强[J].计算机工程,2025,51(6):38-48,11.基金项目
国家自然科学基金(62302084) （62302084）
中国博士后科学基金(2023M730518) （2023M730518）
中央高校基本科研业务费专项资金(N232405-16). （N232405-16）
