Distributed social graph embedding

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.