四川大学学报（自然科学版）2024，Vol.61Issue(4)：3-13,11.DOI:10.19907/j.0490-6756.2024.040001
基于合并子图的双通道跨网络用户身份识别
Two-channel cross-network user identity linkage based on merged subgraph
摘要
Abstract
The essence of cross-social network user identity linkage(UIL)is to discover the same user or en-tity across different social platforms through various methods.The latest idea of existing methods is mainly to aggregate various structural or attribute features of nodes in the network,and then build corresponding deep learning models to learn the similarity of features for the same user in different networks,thereby achieving alignment of the same user in different networks.However,most methods rarely consider user attribute infor-mation or only use a single method to handle different types of attribute features,resulting in the inability to perfectly capture the effective features in attribute texts.Additionally,existing methods learn from the embed-ding space of two networks separately and then map them to a common space,which only captures informa-tion from each network.This paper proposes a new method,namely Two-Channel Cross-Network User Iden-tity Linking Based on Merged Subgraphs(TCUIL).To address the issue of obtaining singular node features,a multi-dimensional feature extraction method is proposed to handle different features using different methods.To solve the problem of non-intersecting embedding spaces in two networks,a graph merging method is pro-posed to facilitate interaction between the information in the two networks.Furthermore,to learn multi-dimensional information from the two networks,a two-channel network structure is designed to effectively learn the network topology,attribute features,and inter-node relationship features.Through extensive experi-ments on two real datasets,the proposed method has been shown to outperform state-of-the-art alignment methods.We conducted extensive experiments on two real datasets(social network and co-authored net-work),achieving at least a 44.32%improvement in F1 for the social network and at least a 25.04%im-provement for the co-authored network.关键词
社交网络/用户身份识别/合并子图/图神经网络Key words
Social network/User identity linkage/Merged subgraph/Graph neural network分类
信息技术与安全科学引用本文复制引用
周小涵,贾鹏,杨频,寇蒋恒,刘鑫哲..基于合并子图的双通道跨网络用户身份识别[J].四川大学学报（自然科学版）,2024,61(4):3-13,11.基金项目
四川省科技厅重点研发项目(2021YFG0156) （2021YFG0156）
