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Community detection in sparse networks via Grothendieck's inequality

Abstract : We present a simple and flexible method to prove consistency of semidefinite optimization problems on random graphs. The method is based on Grothendieck's inequality. Unlike the previous uses of this inequality that lead to constant relative accuracy, we achieve any given relative accuracy by leveraging randomness. We illustrate the method with the problem of community detection in sparse networks, those with bounded average degrees. We demonstrate that even in this regime, various natural semidefinite programs can be used to recover the community structure up to an arbitrarily small fraction of misclas-sified vertices. The method is general; it can be applied to a variety of stochastic models of networks and semidefinite programs.
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Submitted on : Tuesday, January 26, 2016 - 10:17:29 PM
Last modification on : Saturday, January 15, 2022 - 4:08:14 AM
Long-term archiving on: : Wednesday, April 27, 2016 - 1:19:47 PM


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  • HAL Id : hal-01262623, version 1



Olivier Guédon, Roman Vershynin. Community detection in sparse networks via Grothendieck's inequality. Probability Theory and Related Fields, Springer Verlag, 2016, 165 (3-4), pp.1025-1049. ⟨hal-01262623⟩



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