论文标题
深度混合：使用图神经网络重建流行病的演变
Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks
论文作者
论文摘要
我们研究了流行病的时间重建，而不是网络发展。鉴于流行病的部分或汇总的时间信息，我们的目标是估计利用网络拓扑的传播的完全演变，但对精确的流行模型不可知。我们通过数据驱动的解决方案来克服这种缺乏模型意识的方法。特别是，我们提出了DDMIX，这是一种有条件的变异自动编码器，可以从过去的流行病差异中训练，并且其潜在空间旨在捕获基础（未知）扩散动力学的关键方面。我们说明了DDMIX的准确性和普遍性，并通过对合成和现实世界网络模拟的流行病差异进行数字实验将其与非图形的学习算法进行了比较。
We study the temporal reconstruction of epidemics evolving over networks. Given partial or aggregated temporal information of the epidemic, our goal is to estimate the complete evolution of the spread leveraging the topology of the network but being agnostic to the precise epidemic model. We overcome this lack of model awareness through a data-driven solution to the inverse problem at hand. In particular, we propose DDmix, a graph conditional variational autoencoder that can be trained from past epidemic spreads and whose latent space seeks to capture key aspects of the underlying (unknown) spreading dynamics. We illustrate the accuracy and generalizability of DDmix and compare it with non-graph-aware learning algorithms through numerical experiments on epidemic spreads simulated on synthetic and real-world networks.
