recommendations and link prediction [@Gilbert2009], and we employ the previously understudied multiplex properties of OSNs to such ends in this work.

Link Prediction
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The problem of link prediction was first introduced in the seminal work of Kleinberg et al. [@liben2007link] and since then, has been applied in various network domains. For instance, in [@scellato2011exploiting] the authors exploit place features in location-based services to recommend friendships, and in [@backstrom2011supervised] a new model based on supervised random walks is proposed to predict new links in Facebook. Most of these works build on features that are endogenous to the system that hosts the social network of users. In our evaluation, however, we train and test on heterogeneous networks. In a similar spirit, the authors in  [@Sadilek2012] show how using both location and social information from the same network significantly improves link prediction. Our approach differs in that it frames the link prediction task in the context of multilayer networks and empirically shows the relationship between two different systems - Foursquare and Twitter - by mining features from both. Before presenting our framework and analysis, we will next state the research questions we are interested in answering through this work.

Research Questions
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