Abstract: Disclosed is a method for detecting communities in massive social networks by means of an agglomerative approach in which core communities are built and gradually clustered in an iterative manner into higher level communities until the algorithm converges (a stop condition is met), whereby it becomes possible to easily trace how the communities are being formed, resulting in an easily explainable model that allows the detection of overlapping communities. The disclosed method starts from data representing social interactions between individuals, building a weighted social graph where the vertices represent individuals and the links represent social relationships between individuals.

Abstract: It is characterised in that it comprises creating with computing means a multidimensional profile of a user including at least a prediction of behaviour of said user and a characterisation of social influence of said user, said prediction of behaviour comprising: a) applying predictive models to individual factors; b) calculating influence received by said user from a social circle, said calculation based at least on previous events and Social Network Analysis Information, said previous events referred to behaviour or behaviours previously adopted by members of said social circle; and said characterisation of social influence comprising simulating a determined behaviour in said user and estimating the effect caused over at least part of said members of said social circle.