a fraction of it, while the global degree may never exceed the sum of the individual layer degrees. This relationship is apparent in the figure, where *the highest degree users are those who have a large number of links which overlap (multiplex links)*. This can be due to the fact that these users are more engaged across the two platforms. We further explore the value of link multiplexity in the following section.

RQ2: Link Multiplexity
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[0.24]{} ![image](twt_aa.pdf){width="\textwidth"}

[0.24]{} ![image](fsq_aa.pdf){width="\textwidth"}

[0.24]{} ![image](uni_aa.pdf){width="\textwidth"}

[0.24]{} ![image](int_aa.pdf){width="\textwidth"}

We study the three types of links as described in our multilayer model above: multiplex links on both Twitter and Foursquare, which we denote as *tf* for simplicity; single-layer links on Foursquare only (denoted as *fo*); single-layer links on Twitter only (denoted as *to*), and compare these to unconnected pairs of users (denoted as *na*). We consider reciprocal Twitter links only, where $e_{ij}, e_{ji} \in E^T$. Reciprocal relationships in Twitter have been considered as equivalent to undirected ones in other OSNs [@Kwak2010].

Multiplexity and Neighbourhoods
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The number of common friends has been shown to be an important indicator of a link in social networks [@liben2007link]. Moreover, the neighbourhood overlap weighted on the popularity of common 