  In this paper we propose a Bayesian nonparametric approach to modelling
sparse time-varying networks. A positive parameter is associated to each node
of a network, which models the sociability of that node. Sociabilities are
assumed to evolve over time, and are modelled via a dynamic point process
model. The model is able to capture long term evolution of the sociabilities.
Moreover, it yields sparse graphs, where the number of edges grows
subquadratically with the number of nodes. The evolution of the sociabilities
is described by a tractable time-varying generalised gamma process. We provide
some theoretical insights into the model and apply it to three datasets: a
simulated network, a network of hyperlinks between communities on Reddit, and a
network of co-occurences of words in Reuters news articles after the September
11th attacks.
