数据与计算发展前沿2026，Vol.8Issue(1)：103-118,16.DOI:10.11871/jfdc.issn.2096-742X.2026.01.009
基于融合特征的VGAT-VGAN跨社交网络身份关联算法
VGAT-VGAN Across Social Networks User Identity Linkage Algorithm Based on Fusion Features
摘要
Abstract
[Purpose]The research on user identification across social networks is mainly to determine whether virtual users from different social networks belong to the same natural person.[Methods]To address the imbalance between positive and negative samples,firstly,the FD-Struc2vec and DW-Word2vec algorithms are proposed to extract the structural features of nodes and the text features of user names,respectively.Secondly,VGAT is used to optimize the structural feature representation,and the two types of features are fused to form a new user fea-ture vector representation.Meanwhile,VGAN is used to increase the number of positive sam-ples.Finally,Feature-MLP is proposed,which assigns different weights to structural features and text features in the neural network to realize user identification.[Results]Compared with the baseline algo-rithms such as WLAlign in real data sets,the results show that there is an improvement of more than 10% in the three indexes of P,R and F1 value,which proves the effectiveness of the algorithm.[Limitations]Because social networks have a large number of users and complex friendships,coupled with the complexity of the algorithm structure,the overall computing demand is large,and the efficiency of the algorithm needs to be improved.关键词
跨社交网络/身份关联/特征融合/数据增强/深度学习Key words
across social networks/user identity linkage/feature fusion/data enhancement/deep learning引用本文复制引用
潘语泉,袁得嵛,贾源,王安然..基于融合特征的VGAT-VGAN跨社交网络身份关联算法[J].数据与计算发展前沿,2026,8(1):103-118,16.基金项目
公安部技术研究计划重点项目(2024JSZ01) （2024JSZ01）
中国人民公安大学基本科研业务费重点项目(2022JKF02007) （2022JKF02007）
