very few link prediction works tackle multiple networks at a time [@lee2014; @Tang2012], while *most link prediction systems only employ features internal to the network under prediction*, without considering additional link information from other OSNs.

Our main contributions can be summarised as follows:

-   We generalise the notion of a *multilayer online social network*, and extend definitions of neighbourhood to span multiple networks, adapting measures of overlap such as the Adamic/Adar coefficient in social networks to the multilayer context.\

-   We find that *pairs with links on both Twitter and Foursquare exhibit significantly higher interaction on both social networks* in terms of number of mentions and colocation within the same venues, as well as a lower distance and higher number of common hashtags in their tweets.

-   A significantly *higher overlap can be observed between the neighbourhoods of nodes with links on both networks*, in particular with relation to the Adamic/Adar measure of neighbourhood overlap, which is significantly more expressed in the multilayer neighbourhood.

-   In our evaluation, *we predict Twitter links from Foursquare features and vice versa*, and we achieve this with AUC scores up to 0.86 on the different datasets. In predicting links which span both networks, we 