mlsanityData to 7 Oct 2026

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

arXiv 2102.08100 ↗First posted 16 Feb 2021cs.LG cs.AIcited by 19

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Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 16 Feb 2021.

Builds on

11 of 23 references.

References · 11

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Graph Attention Networkscited by 8,854
  5. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  6. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  7. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  8. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
  9. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 273
  10. 2020Scaling Graph Neural Networks with Approximate PageRankcited by 174
  11. 2020Pathfinder Discovery Networks for Neural Message Passingcited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 5; 2025: 2; 2026: 4.

19 papers.

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  1. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  2. 2021Non-separable Spatio-temporal Graph Kernels via SPDEscited by 11
  3. 2022Temporal Multiresolution Graph Neural Networks For Epidemic Predictioncited by 11
  4. 2022Time-series Transformer Generative Adversarial Networkscited by 10
  5. 2023Temporal Graph Neural Networks for Irregular Datacited by 7
  6. 2024A Survey of Transformer Enabled Time Series Synthesiscited by 5
  7. 2026Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemicscited by 4
  8. 2022Seeking Interpretability and Explainability in Binary Activated Neural Networkscited by 3
  9. 2024Learning Regularization for Graph Inverse Problemscited by 3
  10. 2025PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Trainingcited by 3
  11. 2022Towards Automatic Forecasting: Evaluation of Time-Series Forecasting Models for Chickenpox Cases Estimation in Hungarycited by 2
  12. 2023Multi-view Kernel PCA for Time series Forecastingcited by 2
7 more
  1. 2026EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecastingcited by 1
  2. 2024Online Algorithm for Node Feature Forecasting in Temporal Graphscited by 0
  3. 2024A Deep Learning Framework for Evaluating Dynamic Network Generative Models and Anomaly Detectioncited by 0
  4. 2024Information propagation dynamics in Deep Graph Networkscited by 0
  5. 2025Graph Structure Learning with Privacy Guarantees for Open Graph Datacited by 0
  6. 2026Deep Generative Transformers for Probabilistic Time Series and Spatiotemporal Forecastingcited by 0
  7. 2026Spatio-temporal stochastic graph-based learning for infectious disease forecastingcited by 0