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Deep learning of contagion dynamics on complex networks

Author

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  • Charles Murphy

    (Université Laval
    Université Laval)

  • Edward Laurence

    (Université Laval
    Université Laval)

  • Antoine Allard

    (Université Laval
    Université Laval)

Abstract

Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where effective local mechanisms governing a dynamic on a network are learned from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using different contagion dynamics of increasing complexity. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Finally, we illustrate the applicability of our approach using real data of the COVID-19 outbreak in Spain. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.

Suggested Citation

  • Charles Murphy & Edward Laurence & Antoine Allard, 2021. "Deep learning of contagion dynamics on complex networks," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24732-2
    DOI: 10.1038/s41467-021-24732-2
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    Cited by:

    1. Chang Liu & Fengli Xu & Chen Gao & Zhaocheng Wang & Yong Li & Jianxi Gao, 2024. "Deep learning resilience inference for complex networked systems," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Charles Murphy & Vincent Thibeault & Antoine Allard & Patrick Desrosiers, 2024. "Duality between predictability and reconstructability in complex systems," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    3. Tian, Yang & Zhu, Xuzhen & Yang, Qiwen & Tian, Hui & Cui, Qimei, 2022. "Propagation characteristic of adoption thresholds heterogeneity in double-layer networks with edge weight distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).

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