140 citations · 215 across the 18 of their papers we have counts for
5 papers · 1 filter
Inferring Multiple Graphical Structures
Julien Chiquet, Yves Grandvalet, Christophe Ambroise
Gaussian Graphical Models provide a convenient framework for representing dependencies between variables. Recently, this tool has received a high interest for the discovery of biol…
Variational Bayesian Inference and Complexity Control for Stochastic Block Models
Pierre Latouche, Etienne Birmele, Christophe Ambroise
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 pa…
Clustering based on Random Graph Model embedding Vertex Features
Hugo Zanghi, Stevenn Volant, Christophe Ambroise
Large datasets with interactions between objects are common to numerous scientific fields (i.e. social science, internet, biology...). The interactions naturally define a graph and…
Overlapping stochastic block models with application to the French political blogosphere
Pierre Latouche, Etienne Birmelé, Christophe Ambroise
Complex systems in nature and in society are often represented as networks, describing the rich set of interactions between objects of interest. Many deterministic and probabilisti…
Strategies for online inference of model-based clustering in large and growing networks
Hugo Zanghi, Franck Picard, Vincent Miele +1
In this paper we adapt online estimation strategies to perform model-based clustering on large networks. Our work focuses on two algorithms, the first based on the SAEM algorithm,…