  Machine learning is gaining growing momentum in various recent models for the
dynamic analysis of information flows in data communications networks. These
preliminary models often rely on off-the-shelf learning models to predict from
historical statistics while disregarding the physics governing the generating
behaviors of these flows. This paper instead introduces Flow Neural Network
(FlowNN) to improve the feature representation with learned physical bias. This
is implemented by an induction layer, working upon the embedding layer, to
impose the physics connected data correlations, and a self-supervised learning
strategy with stop-gradient to make the learned physics universal. For the
short-timescale network prediction tasks, FlowNN achieves 17% - 71% of loss
decrease than the state-of-the-art baselines on both synthetic and real-world
networking datasets, which shows the strength of this new approach.
