constructed from properties across social networks, perform better than their single network counterparts in predicting links across networks. We propose that combining information from multiple networks in a multilayer configuration can provide new insights into user interactions on online social networks, and can significantly improve link prediction overall with valuable applications to social bootstrapping and friend recommendations.'
author:
- |
    Desislava Hristova$^*$, Anastasios Noulas$^*$, Chloë Brown$^*$, Mirco Musolesi$^{**}$, Cecilia Mascolo$^*$\
    $^*$Computer Laboratory, University of Cambridge\
    $^{**}$School of Computer Science, University of Birmingham\
    `dh475,an346,clb76,cm542 @ cam.ac.uk; m.musolesi @ cs.bham.ac.uk`
title: |
    A Multilayer Approach to Multiplexity and Link Prediction in\
    Online Geo-Social Networks
---

Introduction
============

Online social media has become an ecosystem of overlapping and complementary social networking services, inherently multiplex in nature, as multiple links may exist between the same pair of users [@kivela2013]. Multiplexity is a well studied property in the social sciences [@haythornthwaite1998] and it has been explored in social networks from Renaissance Florence [@Padgett06organizationalinvention] to the Internet age [@hay2005]. Despite the broad contextual differences, multi-relational ties are consistently found to exhibit greater intensity of interactions across different communication channels, and therefore a stronger bond [@haythornthwaite1998; @me2014]. Nevertheless, *there is a lack of research about online social 