221 citations · 434 across the 31 of their papers we have counts for
7 papers · 1 filter
Hoeffding-type decomposition for -statistics on bipartite networks
Tâm Le Minh, Sophie Donnet, François Massol +1
We consider a broad class of random bipartite networks, the distribution of which is invariant under permutation within each type of nodes. We are interested in -statistics defi…
Motif-based tests for bipartite networks
Sarah Ouadah, Pierre Latouche, Stéphane Robin
Bipartite networks are a natural representation of the interactions between entities from two different types. The organization (or topology) of such networks gives insight to unde…
Degree-based goodness-of-fit tests for heterogeneous random graph models : independent and exchangeable cases
Sarah Ouadah, Stéphane Robin, Pierre Latouche
The degrees are a classical and relevant way to study the topology of a network. They can be used to assess the goodness-of-fit for a given random graph model. In this paper we int…
A robust approach for estimating change-points in the mean of an AR(1) process
Souhil Chakar, Émilie Lebarbier, Céline Lévy-Leduc +1
We consider the problem of multiple change-point estimation in the mean of a Gaussian AR(1) process. Taking into account the dependence structure does not allow us to use the dynam…
Modeling heterogeneity in random graphs through latent space models: a selective review
Catherine Matias, Stéphane Robin
We present a selective review on probabilistic modeling of heterogeneity in random graphs. We focus on latent space models and more particularly on stochastic block models and thei…
Classification and estimation in the Stochastic Block Model based on the empirical degrees
Antoine Channarond, Jean-Jacques Daudin, Stéphane Robin
The Stochastic Block Model (Holland et al., 1983) is a mixture model for heterogeneous network data. Unlike the usual statistical framework, new nodes give additional information a…