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In recent decades, the emergence of social networks has enabled internetservice providers (e.g., Facebook, Twitter and Uber) to achieve greatcommercial success. Link prediction is recognized as a common practice to buildthe topology of social networks and keep them evolving. Conventionally, linkprediction methods are dependent of location information of users, whichsuffers from information leakage from time to time. To deal with this problem,companies of smart devices (e.g., Apple Inc.) keeps tightening their privacypolicy, impeding internet service providers from acquiring locationinformation. Therefore, it is of great importance to design location free linkprediction methods, while the accuracy still preserves. In this study, a novellocation free link prediction method is proposed for complex social networks.Experiments on real datasets show that the precision of our location free linkprediction method increases by 10 percent.

A Novel Location Free Link Prediction in Multiplex Social Networks