2 and 4, where the *global neighbourhood* is equivalent to the union of neighbours on both networks, and the *core neighbourhood* is equivalent to the intersection of neighbours across both networks. In this section we consider how the degrees relate to user activity and each other.

In both cases (Figures \[fig:ideg\] and \[fig:udeg\]), users with high activity on both networks, and in particular with high Twitter activity, have the highest degrees in both the core and global neighbourhoods. When we compare the two in Figure \[fig:uvi\], we observe that their joint distribution follows the long-tail exhibited in single-layer social networks as well. Further, we observe the multiplex overlap ratio of the core to global neighbourhood degrees in Figure \[fig:odeg\]. This is simply the core over the global degree:

$$mor_{i} = \frac{k_{CNi}}{k_{GNi}}$$

which indicates the percent of multiplex links in $i$’s multilayer neighbourhood. High activity nodes across both layers at the centre of Figure \[fig:odeg\] have the highest overlap.

In Figure \[fig:uvi\], we compare the two multilayer degrees. We note that the majority of users have a low degree in both, and there is a relationship between the two. The core degree is bound by the global degree and is always 