[0.22]{} ![Interaction features for the different link types.[]{data-label="fig:interactions"}](dist_df.pdf "fig:"){width="\textwidth"}

[0.22]{} ![Interaction features for the different link types.[]{data-label="fig:interactions"}](ints.pdf "fig:"){width="\textwidth"}

[0.22]{} ![Interaction features for the different link types.[]{data-label="fig:interactions"}](dist_hasht.pdf "fig:"){width="\textwidth"}

Although we base our analysis on only two of many possible communication channels online, we are nonetheless able to observe the greater overlap of neighbourhoods and higher intensity of interaction characteristic of multiplex links, which is in consistency with the theory of media multiplexity [@hay2005]. We evaluate the predictive performance of the union of the features presented in the following section.

Multiplexity & Link Prediction
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In this section we address the link prediction problem across layers of social networks, and aim to answer our final two research questions: *Can we predict one network using information from the other?*, and *Can we predict multiplex links in OSNs?* We evaluate the likelihood of forming a social tie as a process that depends on a union of factors, using the Foursquare, Twitter, and multilayer features we have defined up until now in a supervised learning approach, and comparing their predictive power in terms of AUC scores for each feature set respectively.

[0.19]{} ![image](fsq_from_twt.pdf){width="\textwidth"}

[0.19]{} ![image](twt_from_fsq.pdf){width="\textwidth"}

[0.19]{} ![image](m_from_twt.pdf){width="\textwidth"}

[0.19]{} ![image](m_from_fsq.pdf){width="\textwidth"}

[0.19]{} ![image](m_from_m.pdf){width="\textwidth"}

Prediction Space
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The 