platforms possess on them) could lead to more performing steering, nudging and price discrimination practices, we show how social network science has became crucial in predicting consumers consumption patterns in the light of networks features which can be better understood through some measures that we propose below.

Methodological prerequisites {#theory}
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The next sections are devoted to the conceptualisation of our proposal and we therefore provide further below some preliminary notations, for the convenience of the reader.

The classical mathematical abstraction of a network is a graph $G = (V,E)$, where $V$ is the set of $n$ nodes (or vertices) and $E$ is the set of $m$ links (or edges) outlining the relationships among the nodes. For instance, taking into account the network of users in an online marketplace, nodes will be made up of individuals linked among them by the purchased items. Conversely, should, for the same online marketplace, be considered the network of items, nodes will represent goods or services linked together because bundled or, in an alternative setting, purchased by the same user. In this regard, Figure \[fig:1\] provides a basic example of a users’ graph relating to a generic online marketplace with $6$ nodes corresponding to 