Graph drawing by force-directed placement
Software—Practice & Experience
The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A decentralized algorithm for spectral analysis
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Vivaldi: a decentralized network coordinate system
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Interactive Visualization of Small World Graphs
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
ACM Transactions on Computer Systems (TOCS)
Recovering the Long-Range Links in Augmented Graphs
SIROCCO '08 Proceedings of the 15th international colloquium on Structural Information and Communication Complexity
Epidemic-Style management of semantic overlays for content-based searching
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
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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.