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- P. Latouche, E. Birmelé
- 2010

It is now widely accepted that knowledge can be acquired from networks by clustering their vertices according to connection profiles. Many methods have been proposed and in this paper we concentrate on the Stochastic Block Model (SBM). The clustering of vertices and the estimation of SBM model parameters have been subject to previous work and numerous… (More)

Networks are used in many scientific fields such as biology, social science, and information technology. They aim at modelling, with edges, the way objects of interest, represented by vertices, are related to each other. Looking for clusters of vertices, also called communities or modules, has appeared to be a powerful approach for capturing the underlying… (More)

In the last two decades many random graph models have been proposed to extract knowledge from networks. Most of them look for communities or, more generally, clusters of vertices with homogeneous connection profiles. While the first models focused on networks with binary edges only, extensions now allow to deal with valued networks. Recently, new models… (More)

- Marco Corneli, Pierre Latouche, Fabrice Rossi
- 2015 IEEE/ACM International Conference on…
- 2015

The stochastic block model (SBM) [1] describes interactions between nodes of a network following a probabilistic approach. Nodes belong to hidden clusters and the probabilities of interactions only depend on these clusters. Interactions of time varying intensity are not taken into account. By partitioning the whole time horizon, in which interactions are… (More)

- Pierre Latouche, Etienne Birmelé, Christophe Ambroise
- Handbook of Mixed Membership Models and Their…
- 2014

- Pierre Latouche, Fabrice Rossi
- ArXiv
- 2015

Graphs are commonly used to characterise interactions between objects of interest. Because they are based on a straightforward formalism, they are used in many scientific fields from computer science to historical sciences. In this paper, we give an introduction to some methods relying on graphs for learning. This includes both unsupervised and supervised… (More)

- Pierre Latouche, Pierre-Alexandre Mattei, Charles Bouveyron, Julien Chiquet
- J. Multivariate Analysis
- 2016

We address the problem of Bayesian variable selection for high-dimensional linear regression. We consider a generative model that uses a spike-and-slab-like prior distribution obtained by multiplying a deterministic binary vector, which traduces the sparsity of the problem, with a random Gaussian parameter vector. The originality of the work is to consider… (More)

In recent years, many clustering methods have been proposed to extract information from networks. The principle is to look for groups of vertices with homogenous connection profiles. Most of these techniques are suitable for static networks, that is to say, not taking into account the temporal dimension. This work is motivated by the need of analyzing… (More)

- Laetitia Nouedoui, Pierre Latouche
- ESANN
- 2013

We present a non parametric bayesian inference strategy to automatically infer the number of classes during the clustering process of a discrete valued random network. Our methodology is related to the Dirichlet process mixture models and inference is performed using a Blocked Gibbs sampling procedure. Using simulated data, we show that our approach… (More)

It is now widely accepted that knowledge can be learnt from networks by clustering their vertices according to connection profiles. Many deterministic and probabilistic methods have been developed. Given a network, almost all them partition the vertices into disjoint clusters. However, recent studies have shown that these methods were too restrictive and… (More)