Abstract:Spatiotemporal modeling has evolved beyond simple time series analysis to become fundamental in structural time series analysis. While current research extensively employs graph neural networks (GNNs) for spatial feature extraction with notable success, these networks are limited to capturing only pairwise relationships, despite real-world networks containing richer topological relationships. Additionally, GNN-based models face computational challenges that scale with graph complexity, limiting their applicability to large networks. To address these limitations, we present Modern Structure-Aware Simplicial SpatioTemporal neural network (ModernSASST), the first approach to leverage simplicial complex structures for spatiotemporal modeling. Our method employs spatiotemporal random walks on high-dimensional simplicial complexes and integrates parallelizable Temporal Convolutional Networks to capture high-order topological structures while maintaining computational efficiency. Our source code is publicly available on GitHub\footnote{Code is available at: this https URL.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.15833 [cs.LG] |
| (or arXiv:2604.15833v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.15833 arXiv-issued DOI via DataCite (pending registration) |
From: Vincent Gauthier [view email]
[v1]
Fri, 17 Apr 2026 08:35:34 UTC (2,839 KB)
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