  Dynamic temporal graphs represent evolving relations between entities, e.g.
interactions between social network users or infection spreading. We propose an
extension of graph echo state networks for the efficient processing of dynamic
temporal graphs, with a sufficient condition for their echo state property, and
an experimental analysis of reservoir layout impact. Compared to temporal graph
kernels that need to hold the entire history of vertex interactions, our model
provides a vector encoding for the dynamic graph that is updated at each
time-step without requiring training. Experiments show accuracy comparable to
approximate temporal graph kernels on twelve dissemination process
classification tasks.
