MMR-GNN: Multi-Modal Recurrent Neural Networks for Spatiotemporal Forecasting (paper from PAKDD 2024)
Majeske, Nicholas, and Ariful Azad. "Multi-modal Recurrent Graph Neural Networks for Spatiotemporal Forecasting." Pacific-Asia Conference on Knowledge Discovery and Data Mining. Singapore: Springer Nature Singapore, 2024.
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| Figure 1. Encoder-Decoder Architecture of MMR-GNN |
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| Figure 2. Graph Augmentation Layer (GraphAugr) | Figure 3. Spatiotemporal Gated Recurrent Unit (stGRU) |
This repository contains only MMR-GNN implemented in PyTorch. For all experimental design/implementation including datasets, baseline models, and ablation studies, please refer to [MMR-GNN Dev] .
- Python>=3.9
- PyTorch 1.12.0
@inproceedings{majeske2024multi,
title={Multi-modal Recurrent Graph Neural Networks for Spatiotemporal Forecasting},
author={Majeske, Nicholas and Azad, Ariful},
booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
pages={144--157},
year={2024},
organization={Springer}
}