1. A method executed by a processor for calculating similarity information for users of a network, the method comprising: constructing a graph from user interaction data, wherein the graph links the users of the network according to their interaction; weighting links between the users according to strength of interaction between the users; for each of a plurality of users, identifying from among other users with whom a user interacts: a maximum similarity user to whom the user is connected via a maximum weight link, and a minimum similarity user to whom the user is connected via a minimum weight link; assembling a plurality of relative similarity triplets, each triplet comprising a user, that user's identified maximum similarity user connected via a maximum weight link to that user, and that user's identified minimum similarity user connected via a minimum weight link to that user; calculating, for each of the assembled plurality of relative similarity triplets, a difference between a weight of the maximum weight link connecting the user to that user's identified maximum similarity user and a weight of the minimum weight link connecting the user to that user's identified minimum similarity user; selecting representative triplets from among the assembled plurality of relative similarity triplets for which the calculated difference between the weight of the maximum weight link and the weight of the minimum weight link satisfies a threshold difference value; selecting triplets of users exhibiting bias behaviour towards at least one of their maximum or minimum similarity users: identifying all of the user links to the other users, and removing from consideration the maximum and minimum weight links; and identifying those users for whom the evenness of distribution of remaining link weights is above a threshold level, determining a distance metric from the selected representative triplets using a machine learning model; and applying the determined distance metric to a numerical representation of user features to calculate similarity between the users of the network.