  We propose a novel way of modelling time-varying networks, by inducing
two-way sparsity on local models of node connectivity. This two-way sparsity
separately promotes sparsity across time and sparsity across variables (within
time). Separation of these two types of sparsity is achieved through a novel
prior structure, which draws on ideas from the Bayesian lasso and from copula
modelling. We provide an efficient implementation of the proposed model via a
Gibbs sampler, and we apply the model to data from neural development. In doing
so, we demonstrate that the proposed model is able to identify changes in
genomic network structure that match current biological knowledge. Such changes
in genomic network structure can then be used by neuro-biologists to identify
potential targets for further experimental investigation.
