Modeling heterogeneity in random graphs through latent space models: a selective review\*
Page 1 ESAIM: PROCEEDINGS AND SURVEYS, December 2014, Vol. 47, p. 55-74 F …

Statistical node clustering in discrete time dynamic networks is an emerging field that raises
many challenges. Here, we explore statistical properties and frequentist inference in a …

As the coronavirus disease 2019 outbreak evolves, statistical network analysis is playing an
essential role in informing policy decisions. Therefore, researchers who are new to such …

The stochastic block model (SBM) is a mixture model for the clustering of nodes in networks.
The SBM has now been employed for more than a decade to analyze very different types of …

We review the broad range of recent statistical work in social network models, with emphasis
on computational aspects of these methods. Particular focus is applied to exponential-family …

We describe a network clustering framework, based on finite mixture models, that can be
applied to discrete-valued networks with hundreds of thousands of nodes and billions of …

We propose a weighted stochastic block model (WSBM) which extends the stochastic block
model to the important case in which edges are weighted. We address the parameter …

A simple stochastic process is described which provides a useful basis for generating some
types of random networks. The process is based on an iterative building block technique that …

Random-graph mixture models are very popular for modelling real data networks.
Parameter estimation procedures usually rely on variational approximations, either …

We prove identifiability of parameters for a broad class of random graph mixture models.
These models are characterized by a partition of the set of graph nodes into latent …