users’ activities – hence referred to the targeted node – with those gathered from the nodes’ neighbourhood and by exploit via networks’ properties (see paragraph 4).

In this regard, we consider that an underlying network expressing the item-user relationship is always potentially available to platforms, since these latter can rely on two types of networks: an explicit one (used by Facebook and any other social networks whereby network are built and inter-connected) and an implicit one, where the established connections among users are build on their interests or, in general, on their online behaviour [@zhou2014social].

Thanks to network science it is indeed possible to understand the relations between items[^9], not by measuring the distance ($k$-NN) between individuals or goods and/or services within a cluster, but by considering the totality of connections between nodes (which may represent both items and users) in the entire network (e.g. see @birke2013social and @lu2015recommender and references therein). The user-user CF method, for example, implies that it is possible profile individuals by inferring the data they ceded so to put them in a group of subjects with similar characteristics, in order to proceed to $k$-NN measurements and find out what the nearest neighbours are. In the 