mlsanityData to 7 Oct 2026

Temporal Multiresolution Graph Neural Networks For Epidemic Prediction

arXiv 2205.14831 ↗First posted 30 May 2022cs.LG cs.AI cs.SIcited by 11

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Abstract (arXiv)

In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph structures and incorporates the time-series signals to capture the temporal changes of the dynamic graphs. We have applied our proposed model to the task of predicting future spreading of epidemic and pandemic based on the historical time-series data collected from the actual COVID-19 pandemic and chickenpox epidemic in several European countries, and have obtained competitive results in comparison to other previous state-of-the-art temporal architectures and graph learning algorithms. We have shown that capturing the multiscale and multiresolution structures of graphs is important to extract either local or global information that play a critical role in understanding the dynamic of a global pandemic such as COVID-19 which started from a local city and spread to the whole world. Our work brings a promising research direction in forecasting and mitigating future epidemics and pandemics.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 24 of its 58 references that are in the map. First posted on arXiv 30 May 2022.

Builds on

24 of 58 references.

References · 24

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12 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 3; 2026: 1.

11 papers.

  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2023Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's studycited by 6
  3. 2024TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understandingcited by 2
  4. 2023Multimodal Graph Learning for Modeling Emerging Pandemics with Big Datacited by 1
  5. 2026Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasetscited by 1
  6. 2023Sparsity exploitation via discovering graphical models in multi-variate time-series forecastingcited by 0
  7. 2024Learning to Solve Multiresolution Matrix Factorization by Manifold Optimization and Evolutionary Metaheuristicscited by 0
  8. 2024Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecastingcited by 0
  9. 2025MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image Classificationcited by 0
  10. 2025Spectral Contraction of Boundary-Weighted Filters on delta-Hyperbolic Graphscited by 0
  11. 2025Epi2-Net: Advancing Epidemic Dynamics Forecasting with Physics-Inspired Neural Networkscited by 0