no code implementations • 14 Oct 2014 • Shogo Yamanaka, Masayuki Ohzeki, Aurelien Decelle
In this paper we aim to infer the correct biases and interactions of our model by considering a relatively small number of sets of training data.
no code implementations • 14 Sep 2011 • Aurelien Decelle, Florent Krzakala, Cristopher Moore, Lenka Zdeborová
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 communities from the topology of the network.
Statistical Mechanics Disordered Systems and Neural Networks Social and Information Networks Physics and Society
no code implementations • 6 Feb 2011 • Aurelien Decelle, Florent Krzakala, Cristopher Moore, Lenka Zdeborová
We present an asymptotically exact analysis of the problem of detecting communities in sparse random networks.