Abstract: The typical aim of user matching is to detect the same individuals cross different social networks.The existing efforts in this field usually focus on users’ attributes and network embedding,but these methods often ignore the closeness between users and their friends.To this end,we present a friend closeness based user matching algorithm(FCUM).It is a semi-supervised and end-to-end cross social networks user matching algorithm.Attention mechanism is used to quantify the closeness between users and their friends.Quantification of close friends improves the generalization ability of the FCUM.We consider both individual similarity and their close friend similarity by jointly optimizing them in a single objective function.Due to the expensive costs of labeling new match users for training FCUM,we also design a bi-directional matching strategy.Experiments on real datasets illustrate that FCUM outperforms other state-of-the-art methods that only consider the individual similarity.In the situation that the privacy protection of users is becoming more and more strict and it is difficult to obtain other complete attribute information of users,the algorithm has the characteristics of practicality and easy promotion.

Key words: Attention mechanism, Friend closeness, Network embedding, Social networks, User matching