  We propose a new dynamic stochastic blockmodel that focuses on the analysis
of interaction lengths in networks. The model does not rely on a discretization
of the time dimension and may be used to analyze networks that evolve
continuously over time. The framework relies on a clustering structure on the
nodes, whereby two nodes belonging to the same latent group tend to create
interactions and non-interactions of similar lengths. We introduce a fast
variational expectation-maximization algorithm to perform inference, and adapt
a widely used clustering criterion to perform model choice. Finally, we test
our methodology on artificial data, and propose a demonstration on a dataset
concerning face-to-face interactions between students in a high-school.
