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- Aurelien Decelle, Florent Krzakala, Cristopher Moore, Lenka Zdeborová
- Physical review. E, Statistical, nonlinear, and…
- 2011

In this paper we extend our previous work on the stochastic block model, a commonly used generative model for social and biological networks, and the problem of inferring functional groups or… (More)

- Aurelien Decelle, Florent Krzakala, Cristopher Moore, Lenka Zdeborová
- Physical review letters
- 2011

We present an asymptotically exact analysis of the problem of detecting communities in sparse random networks generated by stochastic block models. Using the cavity method of statistical physics and… (More)

Aurelien Decelle, Florent Krzakala, Cristopher Moore, and Lenka Zdeborová Université Paris-Sud & CNRS, LPTMS, UMR8626, Bât. 100, Université Paris-Sud 91405 Orsay, France. CNRS and ESPCI ParisTech, 10… (More)

- Aurelien Decelle, Federico Ricci-Tersenghi
- Physical review letters
- 2014

In this Letter we propose a new method to infer the topology of the interaction network in pairwise models with Ising variables. By using the pseudolikelihood method (PLM) at high temperature, it is… (More)

We introduce a Random Energy Model on a hierarchical lattice where the interaction strength between variables is a decreasing function of their mutual hierarchical distance, making it a nonmean field… (More)

- Lenka Zdeborová, Aurelien Decelle, Michael Chertkov
- Physical review. E, Statistical, nonlinear, and…
- 2009

We use a power grid model with M generators and N consumption units to optimize the grid and its control. Each consumer demand is drawn from a predefined finite-size-support distribution, thus… (More)

- Aurelien Decelle, Pan Zhang
- Physical review. E, Statistical, nonlinear, and…
- 2015

In this paper we study the inference of the kinetic Ising model on sparse graphs by the decimation method. The decimation method, which was first proposed in Decelle and Ricci-Tersenghi [Phys. Rev.… (More)

- Aurelien Decelle, Federico Ricci-Tersenghi
- Physical review. E
- 2016

In this work we explain how to properly use mean-field methods to solve the inverse Ising problem when the phase space is clustered, that is, many states are present. The clustering of the phase… (More)

- Aurelien Decelle, Giancarlo Fissore, Cyril Furtlehner
- ArXiv
- 2017

- Aurelien Decelle, Janina Hüttel, Alaa Saade, Cristopher Moore
- ArXiv
- 2014

These are notes from the lecture of Cristopher Moore given at the autumn school “Statistical Physics, Optimization, Inference, and Message-Passing Algorithms”, that took place in Les Houches, France… (More)