Matching user identities across social networks with limited profile data

查看全文 作　　者：Ildar [1,2]NURGALIEV;Qiang [1]QU;Seyed Mojtaba Hosseini [1,3]BAMAKAN;Muhammad [1,4]MUZAMMAL 机构地区：[1]Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China;[2]Sberbank,Moscow 121170,Russia;[3]Department of Management,Yazd University,Yazd 89195-741,Iran;[4]Department of Computer Science,Bahria University,Islamabad 44000,Pakistan 出　　处：《Frontiers of Computer Science》索引2020年第14卷第6期,共14页 摘　　要：Privacy preservation is a primary concern in social networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age,location,education,interests,and others.The task of matching user identities across different social networks is considered a challenging task.In this work,we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data,i.e,user-name and friendship.Thus,we propose a framework,ExpandUIL,that includes three standalone al-gorithms based on(i)the percolation graph matching in Ex-pand FullName algorithm,(i)a supervised machine learning algorithm that works with the graph embedding,and(ii)a combination of the two,ExpandUserLinkage algorithm.The proposed framework as a set of algorithms is significant as,(i)it is based on the network topology and requires only name feature of the nodes,(i)it requires a considerably low initial seed,as low as one initial seed suffices,(ii)it is iterative and scalable with applicability to online incoming stream graphs,and(iv)it has an experimental proof of stability over a real ground-truth dataset.Experiments on real datasets,Instagram and VK social networks,show upto 75%recall for linked ac-counts with 96%accuracy using only one given seed pair. 关 键 词：social networks user identity linkage graph structure learning,maximum subgraph matching graph percolation