Review for NeurIPS paper: Network Diffusions via Neural Mean-Field Dynamics

The paper investigates how, in a diffusion process, the term accounting for the influence of the whole past, can be modelled in terms of temporal convolution and approximated via a recurrent neural network. The AC thinks that this topic is fully relevant to NeurIPS, and would be of utmost interest for an (admittedly small) fraction of the audience. However, the authors must make every effort to make their work accessible (even for statistical physicists; the Mori-Zwanzig fomalism is perhaps not as well known as the authors think, to say the least) and to thoroughly show how scalable the approach is compared to alternatives. The AC dearly hopes that the authors will invest in the pedagogical and writing efforts required to make their work known to the ML community. Additional emergency review In this paper the authors predict an epidemic process on an unknown network by combining the Generalized Langevin equation (GLE) based on the Mori-Zwanzig formalism, together with deep learning.