Backbone-based Dynamic Spatio-Temporal Graph Neural Network for epidemic forecasting

doi:10.1016/j.knosys.2024.111952

2024-07-01

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DOI：10.1016/j.knosys.2024.111952

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准确的流行病预测是控制流行病传播的关键任务。许多基于深度学习的模型在处理空间信息时只关注静态或动态图，而忽略了它们之间的关系。此外，这些模型通常依赖于递归结构，这可能导致误差累积和计算时间消耗。为了解决上述问题，我们提出了一种新颖的模型，称为基于骨干的动态时空图神经网络 (BDSTGNN)。直观上，图结构的连续平滑变化使得相邻的图结构共享一个基本模式。为了捕获此属性，我们使用自适应方法生成包含主要信息的静态主干图，并使用时态模型生成动态时态图，然后将它们融合以生成基于主干的动态图。为了克服与递归结构相关的潜在限制，我们引入了线性模型DLinear来处理时间依赖性，并将其与动态图卷积相结合以进行流行病预测。在两个数据集上的大量实验表明，BDSTGNN优于基线模型，并且烧蚀比较进一步验证了模型组件的有效性。此外，我们通过使用不同方面的信息度量来分析和衡量骨干图和时间图的重要性。最后，我们验证了BDSTGNN的优越效率。

Keyword:

Epidemic forecasting

Spatio-temporal neural networks

Dynamic graph

Time series decomposition

期刊

Knowledge-Based Systems

IF:

7.6

论文数:

1.3W

被引数:

4.5W

机构

University of Tokyo

学者数:

7.1W

论文数: 6.5W

被引数: 2.2K

shanghai university

学者数:

3.9W

论文数: 2.7W

被引数: 52

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