  We investigate online network topology identification from smooth nodal
observations acquired in a streaming fashion. Different from non-adaptive batch
solutions, our distinctive goal is to track the (possibly) dynamic adjacency
matrix with affordable memory and computational costs by processing signal
snapshots online. To this end, we leverage and truncate dual-based proximal
gradient (DPG) iterations to solve a composite smoothness-regularized,
time-varying inverse problem. Numerical tests with synthetic and real
electrocorticography data showcase the effectiveness of the novel lightweight
iterations when it comes to tracking slowly-varying network connectivity. We
also show that the online DPG algorithm converges faster than a primal-based
baseline of comparable complexity. Aligned with reproducible research
practices, we share the code developed to produce all figures included in this
paper.
