Privacy preservation is a primary concern in so-cial networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user informa-tion 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-pandFullName algorithm,(ii)a supervised machine learning algorithm that works with the graph embedding,and(iii)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,(ii)it requires a considerably low initial seed,as low as one initial seed suffices,(iii)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.