Abstract
Online social systems are multiplex in nature as multiple links may exist between the same two users across different social media. In this work, we study the geo-social properties of multiplex links, spanning more than one social network and apply their structural and interaction features to the problem of link prediction across social networking services. Exploring the intersection of two popular online platforms - Twitter and location-based social network Foursquare - we represent the two together as a composite multilayer online social network, where each platform represents a layer in the network. We find that pairs of users connected on both services, have greater neighbourhood similarity and are more similar in terms of their social and spatial properties on both platforms in comparison with pairs who are connected on just one of the social networks. Our evaluation, which aims to shed light on the implications of multiplexity for the link generation process, shows that we can successfully predict links across social networking services. In addition, we also show how combining information from multiple heterogeneous networks in a multilayer configuration can provide new insights into user interactions on online social networks, and can significantly improve link prediction systems with valuable applications to social bootstrapping and friend recommendations.
Highlights
1 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 [ ]
We explore how we can leverage multiplex tie strength through the geographic and social interactions of users and apply it to the classic networks problem of link prediction [ ]
We explore multilayer networks with heterogeneous layers and apply media multiplexity theory to study the social and geographical features of pairs of users and their application to link prediction across online social networks
Summary
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 [ ]. Multiplexity is a well studied property in the social sciences [ ] and it has been explored in social networks from Renaissance Florence [ ] to the Internet age [ ]. We explore how we can leverage multiplex tie strength through the geographic and social interactions of users and apply it to the classic networks problem of link prediction [ ]. Link prediction systems are key components of social networking services due to their practical applicability to friend recommendations and social network bootstrapping, as well as to understanding the link generation process. Link prediction is a well-studied problem, explored in the context of both OSNs and location-based social networks
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