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Distributed social graph embedding
Citation key KLT-DSGE-11
Author Kermarrec, Anne-Marie and Leroy, Vincent and Trédan, Gilles
Title of Book Proceedings of the 20th Conference on Information and Knowledge Management (CIKM '11)
Pages 1209–1214
Year 2011
ISBN 978-1-4503-0717-8
DOI http://dx.doi.org/10.1145/2063576.2063751
Location Glasgow, UK
Address New York, NY, USA
Month October
Publisher ACM
Abstract Distributed recommender systems are becoming increasingly important for they address both scalability and the Big Brother syndrome. Link prediction is one of the core mechanism in recommender systems and relies on extracting some notion of proximity between entities in a graph. Applied to social networks, defining a proximity metric between users enable to predict potential relevant future relationships. In this paper, we propose SoCS (Social Coordinate Systems), a fully distributed algorithm that embeds any social graph in an Euclidean space, which can easily be used to implement link prediction. To the best of our knowledge, SoCS is the first system explicitly relying on graph embedding. Inspired by recent works on non-isomorphic embeddings, the SoCS embedding preserves the community structure of the original graph, while being easy to decentralize. Nodes thus get assigned coordinates that reflect their social position. We show through experiments on real and synthetic data sets that these coordinates can be exploited for efficient link prediction.
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