Web: http://arxiv.org/abs/2209.06520

Sept. 15, 2022, 1:11 a.m. | Andrea Cini, Ivan Marisca, Filippo Maria Bianchi, Cesare Alippi

cs.LG updates on arXiv.org arxiv.org

Neural forecasting of spatiotemporal time series drives both research and
industrial innovation in several relevant application domains. Graph neural
networks (GNNs) are often the core component of the forecasting architecture.
However, in most spatiotemporal GNNs, the computational complexity scales up to
a quadratic factor with the length of the sequence times the number of links in
the graph, hence hindering the application of these models to large graphs and
long temporal sequences. While methods to improve scalability have been
proposed …

arxiv graph graph neural networks networks neural networks scalable

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