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Almost Exact Recovery in Label Spreading

  • Konstantin Avrachenkov
  • Maximilien DrevetonEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11631)

Abstract

In semi-supervised graph clustering setting, an expert provides cluster membership of few nodes. This little amount of information allows one to achieve high accuracy clustering using efficient computational procedures. Our main goal is to provide a theoretical justification why the graph-based semi-supervised learning works very well. Specifically, for the Stochastic Block Model in the moderately sparse regime, we prove that popular semi-supervised clustering methods like Label Spreading achieve asymptotically almost exact recovery as long as the fraction of labeled nodes does not go to zero and the average degree goes to infinity.

Keywords

Semi-supervised clustering Community detection Label spreading Random graphs Stochastic Block Model 

Notes

Acknowledgements

This work has been done within the project of Inria – Nokia Bell Labs “Distributed Learning and Control for Network Analysis”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Inria Sophia AntipolisValbonneFrance

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