single node with the entire network, our definition allows the estimation of such node’s “network share” by using information related to those nodes to which it is connected. Such approach intends to build on the fact that, generally, each node possesses data [also called [*metadata*]{}, e.g. see @Peele1602548] through which the enterprise can infer collective information about clients (e.g. geographical localisations, preferences, gender, etc.) to be used, for instance, to implement price discrimination practices.

In this regard, we propose to consider the network’s neighbourhood of a node as a proxy of the similarities among clients, as network clustering techniques do [@cerqueti2018new]. In other words, links between nodes attest a certain level of preferences and/or similar tastes, thus making it possible to extend the chain of relationship from the node’s neighbours to the “neighbours of its neighbours” (the “alters of the alters”), shifting the network share of a node from its singularity to its linked environment and, then, to the whole collective patterns of interactions.

Our model builds on the assumption that, if an enterprise intends to gather information about users with similar preferences, it may avoid burdensome techniques (such as cluster analysis or density algorithm), thanks to network properties. In 