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 the 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.
翻译:预测传染性动态的演变仍是一个尚未解决的问题,机械模型只能提供部分答案。 这些模型要保持数学或计算上的可扩展性,就必须依靠简化的假设,从而限制其预测的量化准确性以及它们能够模型的动态的复杂性。在这里,我们提出基于深层学习的补充方法,即从时间序列数据中学习关于网络动态的有效地方机制。我们的图形神经网络结构很少对动态进行假设,我们使用日益复杂的不同传染性动态来证明其准确性。通过允许对任意网络结构进行模拟,我们的方法使得有可能探索所学动态在培训数据之外的特点。最后,我们用西班牙COVID-19爆发的真实数据来说明我们的方法的可适用性。我们的成果表明,深度学习如何为在网络上建立有效传染性动态模型提供了新的互补视角。