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.

Multi-Modal Recurrent Graph Neural Network

Figure 1. Encoder-Decoder Architecture of MMR-GNN

Figure 2. Graph Augmentation Layer (GraphAugr)	Figure 3. Spatiotemporal Gated Recurrent Unit (stGRU)

Primary Results

Experiments

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] .

Requirements

1. Python>=3.9
2. PyTorch 1.12.0

Citation

@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}
}

Contact

nmajeske@iu.edu or azad@iu.edu