现代电子技术2025，Vol.48Issue(2)：51-54,4.DOI:10.16652/j.issn.1004-373x.2025.02.009
基于自适应差异化图卷积的图注意力网络表示学习算法
Graph attention network representation learning algorithm based on adaptive differentiation graph convolution
摘要
Abstract
In order to solve the limitation of traditional graph convolution network in dealing with complex relationships between nodes,a graph attention network representation learning algorithm based on adaptive differentiation graph convolution network is proposed.The differentiation graph convolution network is used to conduct differential sampling according to each node′s own characteristics and neighbor information,so as to capture the complex relationships between nodes.The two-stage key neighbor sampling method is used to mine important nodes first and retain randomness to complete the sampling of key neighbor nodes.In combination with graph attention network,the key neighbor node features are aggregated to their own nodes by means of local attention and adaptive learning weight distribution,so as to enhance the node feature representation.After training the network,the learning ability of network representation is enhanced further.The experimental results show that the proposed algorithm can optimize the degree of node aggregation and boundary clarity,and improve the accuracy and visualization of node classification.The algorithm also shows superior performance in network representation learning by paying attention to second-order neighbors and using double attention.关键词
网络表示学习/图卷积网络/自适应差异化机制/节点采样/特征聚合/网络训练/图注意力网络Key words
network representation learning/graph convolution network/adaptive differentiation mechanism/node sampling/feature aggregation/network training/graph attention network分类
电子信息工程引用本文复制引用
吴誉兰,舒建文..基于自适应差异化图卷积的图注意力网络表示学习算法[J].现代电子技术,2025,48(2):51-54,4.基金项目
江西省教育厅科技项目(GJJ2204309) （GJJ2204309）
