  Spatio-temporal graphs such as traffic networks or gene regulatory systems
present challenges for the existing deep learning methods due to the complexity
of structural changes over time. To address these issues, we introduce
Spatio-Temporal Deep Graph Infomax (STDGI)---a fully unsupervised node
representation learning approach based on mutual information maximization that
exploits both the temporal and spatial dynamics of the graph. Our model tackles
the challenging task of node-level regression by training embeddings to
maximize the mutual information between patches of the graph, at any given time
step, and between features of the central nodes of patches, in the future. We
demonstrate through experiments and qualitative studies that the learned
representations can successfully encode relevant information about the input
graph and improve the predictive performance of spatio-temporal auto-regressive
forecasting models.
