  Online social systems are multiplex in nature as multiple links may exist
between the same two users across different social networks. In this work, we
introduce a framework for studying links and interactions between users beyond
the individual social network. Exploring the cross-section of two popular
online platforms - Twitter and location-based social network Foursquare - we
represent the two together as a composite multilayer online social network.
Through this paradigm we study the interactions of pairs of users
differentiating between those with links on one or both networks. We find that
users with multiplex links, who are connected on both networks, interact more
and have greater neighbourhood overlap on both platforms, in comparison with
pairs who are connected on just one of the social networks. In particular, the
most frequented locations of users are considerably closer, and similarity is
considerably greater among multiplex links. We present a number of structural
and interaction features, such as the multilayer Adamic/Adar coefficient, which
are based on the extension of the concept of the node neighbourhood beyond the
single network. Our evaluation, which aims to shed light on the implications of
multiplexity for the link generation process, shows that multilayer features,
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.
