Abstract
Algorithms for social network user profiling suffer from one or more of the following limitations: (1) assuming that the full social graph is available for training, (2) not exploiting the rich information that is available in social networks such as group memberships and likes, (3) treating numeric attributes as nominal attributes, and (4) not assessing the certainty of its predictions. In this paper, we address these challenges by proposing an improved algorithm named PGPI+ (Partial Graph Profile Inference+). PGPI+ accurately infers user profiles under the constraint of a partial social graph using rich information about users (e.g. group memberships, views and likes), handles nominal and numeric attributes, and assesses the certainty of predictions. An experimental evaluation with more than 30,000 user profiles from the Facebook and Pokec social networks shows that PGPI+ predicts user profiles with considerably more accuracy and by accessing a smaller part of the social graph than five state-of-the-art algorithms.
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Dougnon, R.Y., Fournier-Viger, P., Lin, J.CW., Nkambou, R. (2015). More Accurate Inference of User Profiles in Online Social Networks. In: Pichardo Lagunas, O., Herrera Alcántara, O., Arroyo Figueroa, G. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2015. Lecture Notes in Computer Science(), vol 9414. Springer, Cham. https://doi.org/10.1007/978-3-319-27101-9_41
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DOI: https://doi.org/10.1007/978-3-319-27101-9_41
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