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In this paper the team led by Prof Alippi proposes a scalable spatiotemporal graph neural network architecture that exploits an efficient training-free encoding of both temporal and spatial dynamics. This is particularly relevant since neural forecasting of spatiotemporal time series drives both research and industrial innovation in several relevant application domains.