466 citations · 635 across the 20 of their papers we have counts for
24 papers
Model-based clustering in simple hypergraphs through a stochastic blockmodel
Luca Brusa, Catherine Matias
We propose a model to address the overlooked problem of node clustering in simple hypergraphs. Simple hypergraphs are suitable when a node may not appear multiple times in the same…
Spectral density of random graphs: convergence properties and application in model fitting
Suzana de Siqueira Santos, André Fujita, Catherine Matias
Random graph models are used to describe the complex structure of real-world networks in diverse fields of knowledge. Studying their behavior and fitting properties are still criti…
Properties of the Stochastic Approximation EM Algorithm with Mini-batch Sampling
Tabea Rebafka, Estelle Kuhn, Catherine Matias
To deal with very large datasets a mini-batch version of the Monte Carlo Markov Chain Stochastic Approximation Expectation-Maximization algorithm for general latent variable models…
Nine Quick Tips for Analyzing Network Data
Stephane Robin, Vincent Miele, Catherine Matias +1
These tips provide a quick and concentrated guide for beginners in the analysis of network data.
Consistency of the maximum likelihood and variational estimators in a dynamic stochastic block model
Léa Longepierre, Catherine Matias
We consider a dynamic version of the stochastic block model, in which the nodes are partitioned into latent classes and the connection between two nodes is drawn from a Bernoulli d…
Revealing the hidden structure of dynamic ecological networks
Vincent Miele, Catherine Matias
Recent technological advances and long-term data studies provide interaction data that can be modelled through dynamic networks, i.e a sequence of different snapshots of an evolvin…