in particular a platform, can always build up its own network considering consumers’ data as well as data related to items offered to them. In this regard, a network can be: (i) explicitly available, when the enterprise delivers multi-users products or services (e.g., Facebook, LinkedIn, Twitter and MySpace); (ii) not explicitly available, since the firm delivers single user products or services (e.g. Amazon, Last.FM, Outbrain and Color)[^20].

Among the properties offered by the configuration model, we used – for our purposes – that of the [*average degree of a neighbour*]{}, represented with symbol $k_{nn}$. For a generic node of the network, it holds that [@catanzaro2005generation]:

$$\label{neighbor}
k_{nn} = \frac{\langle d^2\rangle}{\langle d\rangle}$$

The average degree of a neighbour is widely used to study dependencies between neighbours nodes’ degrees in a network, affirming that, considering a node $i$, $k_{nn}$ is independent from the node’s degree but exclusively dependent on the global network’s characteristics $\langle d\rangle$ and $\langle d^2\rangle$. Besides being easily computable, such measures provide an overall view of the network, making $k_{nn}$ a simple and powerful global measure of it.

Generally, $k_{nn}$ turns out to be greater than the average degree of a node. As a matter fact, considering ${\langle d^2\rangle}/{\langle 