  Dynamic spatial graph construction is a challenge in graph neural network
(GNN) for time series data problems. Although some adaptive graphs are
conceivable, only a 2D graph is embedded in the network to reflect the current
spatial relation, regardless of all the previous situations. In this work, we
generate a spatial tensor graph (STG) to collect all the dynamic spatial
relations, as well as a temporal tensor graph (TTG) to find the latent pattern
along time at each node. These two tensor graphs share the same nodes and
edges, which leading us to explore their entangled correlations by Projected
Entangled Pair States (PEPS) to optimize the two graphs. We experimentally
compare the accuracy and time costing with the state-of-the-art GNN based
methods on the public traffic datasets.

Computer Vision and Pattern Recognition