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 the users' attributes and network embedding, but these methods often ignore the closeness between the 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 networks user matching algorithm. Attention mechanism is used to quantify the closeness between users and their friends. We considers both individual similarity and their close friends similarity by jointly optimize them in a single objective function. Quantification of close friends improves the generalization ability of the FCUM. 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.