to study networked systems properties, it is possible to exploit a method allowing the creation of a random network by using a given degree sequence. To this end, one of the most common and used tool is the [*configuration model*]{} [@newman2018networks]. This latter, defined as a generalised random network model whit a given degree sequence, is build up by means of a simple algorithm [e.g. pag. 139, @barabasi2016network].

Configuration model can also be generated considering degree sequences stemming from a given distribution. Thanks to such feature, it is possible to generate specific networks taking into account, e.g, Poisson’s or Power-Law distributions, which are the widely used degree configurations in network science.

The purpose of the configuration model is that of leveraging some of its known properties in order to obtain general results suitable for building a new theoretical framework[^19].

Considering the configuration model properties, we therefore suppose that a platform may generate its own network (thus, the corresponding graph) starting from a database containing a given number of users (therefore, the length of the degree sequence) and their relative purchase choices (hence, the elements within the degree sequence).

In the light of such elements, we suppose that an enterprise, and 