Abstract: The present invention refers to a method for characterizing a group of users, related among them by their mobile communication data, according to web navigation data. The method comprises: building a social graph from the mobile communication data of a user and his contacts; extracting web navigation data of each user of the social graph; associating to each edge linking two users of the social graph, web navigation data extracted for both two users; obtaining a measure of harmony based on comparing web navigation data of both two users; and providing a set of metrics for the group of users based on the measures of harmony, characterizing the group of users.

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.