could decide to offer a certain item instead of another, hiding the existence of perfect substitute goods, nudging individuals to the purchase of a more expensive but equivalent item on the basis of a connections analysis (hence an analysis of the connections between the goods purchased by the individual and those connected to them) aimed at estimating the users’ price sensitivity [@mikians2012detecting; @mattioli2012orbitz][^12].

When asked how e-commerce websites could implement price “personalisation” in relation to each consumer so to squeezing their surplus,  @Hannak2014 [pp. 306] answered that two practices had been conceived by those players: price discrimination – customising prices for some users – \[...\] (and) price steering – changing the order of search results to highlight specific products.

Network science may ensure a more effective implementation of these practices because evaluations made by the platforms on what item to show or not, would be carried out in relation to the whole universe of items available to all users and not only to those addressed – at a given time – to a certain group of individuals. In this context it is therefore appropriate investigating how the use of NS could lead to an evolution of profiling practices grounded on 