A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks

  • Emilie KaufmannEmail author
  • Thomas Bonald
  • Marc Lelarge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9925)


This paper presents a novel spectral algorithm with additive clustering, designed to identify overlapping communities in networks. The algorithm is based on geometric properties of the spectrum of the expected adjacency matrix in a random graph model that we call stochastic blockmodel with overlap (SBMO). An adaptive version of the algorithm, that does not require the knowledge of the number of hidden communities, is proved to be consistent under the SBMO when the degrees in the graph are (slightly more than) logarithmic. The algorithm is shown to perform well on simulated data and on real-world graphs with known overlapping communities.


Spectral Algorithm Stochastic Block Model (SBM) Random Graph Model Pure Node Mixed Membership Stochastic Blockmodels (MMSB) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors acknowledge the support of the French Agence Nationale de la Recherche (ANR) under reference ANR-11-JS02-005-01 (GAP).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.CNRS & CRIStALUniversité LilleVilleneuve-d’ascqFrance
  2. 2.Télécom ParisTechUniversité Paris-SaclayParisFrance
  3. 3.Inria-ENSParisFrance

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