Learning to Rank on Anchor Link across Multiple Heterogeneous Social Networks

With the prevalence of mobile communication techniques, people now spend a lot of time diving in online social networks for various purposes. Social networks thus contain abundant information of users, such as active periods, emerging locations, and interactions with friends. However, as the services social networks provided are orthogonal, no single network comprehensively depicts a user. Recently, a number of researches start to discover the alignments between entities from different social networks. The discovered alignments are valuable as they reveal intentions of users from different perspectives and are helpful to service providers to offer customized services. In this paper, we investigate the alignment problem of users between different social networks. Three aspects of features including profile matching, social relationship and behavior consistency, and techniques of learning to rank with mapping constraints are applied. We resolve the class skewness problem which generally exists in social networks due to the lack of sufficient negative links by learning to rank. Extensive experiments based on two popular heterogeneous social networks (Google+ and Twitter) with evaluation metrics, precision, recall, accuracy and f-measure, are applied to illustrate performance of our method. Also analysis on the features and constraint gives practical implications for future research.