Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks

Benedek Rozemberczki, Paul Scherer, Oliver Kiss, Rik Sarkar, Tamas Ferenci
2/16/2021
cs.LGcs.AI

Official code: benedekrozemberczki/pytorch_geometric_temporal (2,992 stars, MIT, PyTorch, last commit 2025-09-18). 1 more implementation below.

Abstract

Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing. Newly proposed graph neural network architectures are repetitively evaluated on standard tasks such as traffic or weather forecasting. In this paper, we propose the Chickenpox Cases in Hungary dataset as a new dataset for comparing graph neural network architectures. Our time series analysis and forecasting experiments demonstrate that the Chickenpox Cases in Hungary dataset is adequate for comparing the predictive performance and forecasting capabilities of novel recurrent graph neural network architectures.

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Code Implementations(2)

benedekrozemberczki/pytorch_geometric_temporalOfficial100%
PyTorchMIT

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

2,992402PythonJun 27, 20201 years agoMIT
deep-learninggcngnngraph-convolutiongraph-convolutional-networks+14 more
benedekrozemberczki/spatiotemporal_datasetsCommunity100%
PyTorchApache-2.0

Spatiotemporal datasets collected for network science, deep learning and general machine learning research.

645Feb 3, 20219 months agoApache-2.0
analyticsbenchmarkdata-sciencedatasetdeep-learning+14 more

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Cite this paper

@article{rozemberczki2021chickenpox,
  title  = {Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks},
  author = {Benedek Rozemberczki and Paul Scherer and Oliver Kiss and Rik Sarkar and Tamas Ferenci},
  year   = {2021},
  eprint = {2102.08100},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2102.08100}
}

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