现代信息科技2024，Vol.8Issue(10)：64-67,4.DOI:10.19850/j.cnki.2096-4706.2024.10.014
基于二阶图自编码器的复杂网络分析
Analysis of Complex Network Based on Second-order Graph Autoencoder
摘要
Abstract
In order to make full use of the information contained in complex networks and enhance the representation ability of graph autoencoder models,we propose an autoencoder model SeGCN-AE based on Second-order Graph Convolutional Networks(SeGCN).First,SeGCN is used to extract entity attributes and relationship information,and generate low-dimensional feature representations.Then,the inner product decoder is used to reconstruct the complex network link relationship matrix,and the model is optimized by reconstruction loss.On the two baseline complex network dataset experiments,the performance of SeGCN-AE is always better than current advanced baseline model,indicating that the introduction of second-order relationships can enhance representation ability of the model and improve the performance of complex network analysis tasks.关键词
图自编码器/图卷积网络/标签预测/关系预测Key words
graph autoencoder/Graph Convolutional Network/label prediction/relationship prediction分类
信息技术与安全科学引用本文复制引用
袁立宁,刘义江,莫嘉颖,罗恒雨..基于二阶图自编码器的复杂网络分析[J].现代信息科技,2024,8(10):64-67,4.基金项目
广西哲学社会科学研究课题(23FTQ005) （23FTQ005）
广西壮族自治区公安厅专项课题(2023GAQN092) （2023GAQN092）
广西警察学院校级科研项目(2022KYZ17) （2022KYZ17）
