Towards trust inference from bipartite social networks
(2012) Second ACM SIGMOD Workshop on Databases and Social Networks (DBSocial 2012) — Location: Scottsdale, AZ, USA (20.May.2012)
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- Abstract
- The emergence of trust as a key link between users in social networks has provided an effective means of enhancing the personalization of on-line user content. However, the availability of such trust information remains a challenge to the algorithms that use it, as the majority of social networks do not provide a means of explicit trust feedback. This paper presents an investigation into the inference of trust relations between actor pairs of a social network, based solely on the structural information of the bipartite graph typical of most on-line social networks. Using intuition inspired from real life observations, we argue that the popularity of an item in a social graph is inversely related to the level of trust between actor pairs who have rated it. From an existing bipartite social graph, this method computes a new social graph, linking actors together by means of symmetric weighted trust relations. Through a set of experiments performed on a real social network dataset, our method produces statistically significant results, showing strong trust prediction accuracy.
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Citations
O’Doherty, D., Jouili, S., & Van Roy, P. (2012). Towards trust inference from bipartite social networks. In Denilson Barbosa, Kristen LeFevre, Evimaria Terzi (ed.), Proceeding DBSocial ’12 Proceedings of the 2nd ACM SIGMOD Workshop on Databases and Social Networks (p. p. 13-18). https://doi.org/10.1145/2304536.2304539