Scalable Spatiotemporal Graph Neural Networks
- URL: http://arxiv.org/abs/2209.06520v1
- Date: Wed, 14 Sep 2022 09:47:38 GMT
- Title: Scalable Spatiotemporal Graph Neural Networks
- Authors: Andrea Cini, Ivan Marisca, Filippo Maria Bianchi, Cesare Alippi
- Abstract summary: Graph neural networks (GNNs) are often the core component of the forecasting architecture.
In most pretemporal GNNs, the computational complexity scales up to a quadratic factor with the length of the sequence times the number of links in the graph.
We propose a scalable architecture that exploits an efficient encoding of both temporal and spatial dynamics.
- Score: 14.415967477487692
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 in the context of static graphs, few research efforts have been
devoted to the spatiotemporal case. To fill this gap, we propose a scalable
architecture that exploits an efficient encoding of both temporal and spatial
dynamics. In particular, we use a randomized recurrent neural network to embed
the history of the input time series into high-dimensional state
representations encompassing multi-scale temporal dynamics. Such
representations are then propagated along the spatial dimension using different
powers of the graph adjacency matrix to generate node embeddings characterized
by a rich pool of spatiotemporal features. The resulting node embeddings can be
efficiently pre-computed in an unsupervised manner, before being fed to a
feed-forward decoder that learns to map the multi-scale spatiotemporal
representations to predictions. The training procedure can then be parallelized
node-wise by sampling the node embeddings without breaking any dependency, thus
enabling scalability to large networks. Empirical results on relevant datasets
show that our approach achieves results competitive with the state of the art,
while dramatically reducing the computational burden.
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