In light of the related work presented above, our goal is to mend the gap between multilayer network models, media multiplexity properties, and link prediction systems. More specifically, we address the following research questions in this work:\
**RQ1:** *How do structural properties such as degree extend into the multilayer neighbourhood?* We propose a multilayer version of the network neighbourhood, which extends it to multiple networks (layers) and observe how such structural properties are manifested across Twitter and Foursquare.\
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**RQ2:** *What are the structural and behavioural differences between single network and multiplex links?* In order to understand the value of multiplex links (users connected on more than one network), we observe how they compare to single network links in terms of neighbourhood overlap, Twitter interaction, similarity and mobility in Foursquare.\
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**RQ3:** *Can we use information about links from one layer to predict links on the other?* Many online social systems suffer from a lack of initial user adoption. Although many social networks nowadays incorporate the option of importing contacts from another pre-existing network and copying links, this method does not offer a ranking of users by relevance targeted towards the specific platform.\
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**RQ4:** *Can we predict links which 