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Spatiotemporal graph neural networks (STGNNs) have emerged as a leadingapproach for learning representations and forecasting on traffic datasets withunderlying topological and correlational structures. However, current STGNNsuse intricate techniques with high complexities to capture these structures,making them difficult to understand and scale. The existence of simple yetefficient architectures remains an open question. Upon closer examination, wefind what lies at the core of STGNN's representations are certain forms ofspatiotemporal contextualization. In light of this, we design Nexus sine quanon (NexuSQN), an essentially connected network built on an efficientmessage-passing backbone. NexuSQN simply uses learnable "where" and "when"locators for the aforementioned contextualization and omits any intricatecomponents such as RNNs, Transformers, and diffusion convolutions. Results showthat NexuSQN outperforms intricately designed benchmarks in terms of size,computational efficiency, and accuracy. This suggests a promising future fordeveloping simple yet efficient neural predictors.

Nexus sine qua non: Essentially Connected Networks for Traffic Forecasting