Beyond social graphs: mining patterns underlying social interactions

Abstract

This work aims at discovering and extracting relevant patterns underlying social interactions. To do so, some knowledge extracted from Facebook, a social networking site, is formalised by means of an Extended Social Graph, a data structure which goes beyond the original concept of a social graph by also incorporating information on interests. When the Extended Social Graph is built, state-of-the-art techniques are applied over it in order to discover communities. Once these social communities are found, statistical techniques will look for relevant patterns common to each of those, in such a way that each cluster of users is characterised by a set of common features. The resulting knowledge will be used to develop and evaluate a social recommender system, which aims at suggesting users in a social network with possible friends or interests.

Mining communities and their descriptions on attributed graphs: a survey

“…Newman and Clauset (2016) use a Bayesian modeling technique based on stochastic block models for estimating community allocations including structural and attributive information, however no description is targeted. Baldominos et al (2017) find stereotypes from communities detected using a modularity-optimizing algorithm by weighting labels according to the proportion of vertices in the community that support them. Conversely, Martínez-Seis (2017) use homophilic principles for obtaining a ranking of the attributes and then only apply those for community detection.…”