140 citations · 363 across the 28 of their papers we have counts for
5 papers · 1 filter
Modelling time evolving interactions in networks through a non stationary extension of stochastic block models
Marco Corneli, Pierre Latouche, Fabrice Rossi
In this paper, we focus on the stochastic block model (SBM),a probabilistic tool describing interactions between nodes of a network using latent clusters. The SBM assumes that the…
Goodness of fit of logistic models for random graphs
Pierre Latouche, Stéphane Robin, Sarah Ouadah
Logistic regression is a natural and simple tool to understand how covariates contribute to explain the topology of a binary network. Once the model fitted, the practitioner is int…
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…
Graphs in machine learning: an introduction
Pierre Latouche, Fabrice Rossi
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 fr…
Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks
Marco Corneli, Pierre Latouche, Fabrice Rossi
The latent block model (LBM) is a flexible probabilistic tool to describe interactions between node sets in bipartite networks, but it does not account for interactions of time var…