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Beyond social graphs: mining patterns underlying social interactions

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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.

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Notes

  1. Friendship recommendations will only be provided when \(\alpha \ne 0\) %, as otherwise we cannot check the suggested friendship against the set of removed information.

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  1. Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain

    Alejandro Baldominos, Javier Calle & Dolores Cuadra

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  1. Alejandro Baldominos
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  2. Javier Calle
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Correspondence to Alejandro Baldominos.

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Baldominos, A., Calle, J. & Cuadra, D. Beyond social graphs: mining patterns underlying social interactions. Pattern Anal Applic 20, 269–285 (2017). https://doi.org/10.1007/s10044-016-0550-2

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