A Novel Location Free Link Prediction in Multiplex Social Networks

TL;DR

This paper introduces a new location-free link prediction method for multiplex social networks that maintains high accuracy without relying on user location data, addressing privacy concerns.

Contribution

The paper proposes a novel location-free link prediction approach that outperforms existing methods by 10% in precision, enhancing privacy preservation in social network analysis.

Findings

Precision increased by 10% with the new method

Effective in complex multiplex social networks

Addresses privacy issues in link prediction

Abstract

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