从感染级联重建网络
Network reconstruction from infection cascades
摘要：
传播动力学扩散的网络对于理解和控制它是必不可少的。在少数情况下，这些信息可以通过直接实验或由于传播数据的性质而获得。然而，在大多数情况下，关于网络的可用信息是间接的，来自动态的部分观测，使网络重建成为一个基本的逆问题。在这里，我们表明，它是可以重建的互动网络的整个结构，并同时推断激活传播的完整的时间过程中，只依赖于一个单一的时代（即快照）或时间分散的观察少量的活动级联。我们提出的方法是建立在一个信念传播近似，在各种各样的相关情况下，已显示出令人印象深刻的准确性，并能够推断存在不完整的时间序列数据的相互作用，通过提供一个详细的建模条件下的轨迹的后验分布的观察。此外，我们通过实验表明，完整的级联的信息内容是相对较小的稀疏观测或单快照。
英文摘要：
Accessing the network through which a propagation dynamics diffuse is essential for understanding and controlling it. In a few cases, such information is available through direct experiments or thanks to the very nature of propagation data. In a majority of cases however, available information about the network is indirect and comes from partial observations of the dynamics, rendering the network reconstruction a fundamental inverse problem. Here we show that it is possible to reconstruct the whole structure of an interaction network and to simultaneously infer the complete time course of activation spreading, relying just on single epoch (i.e. snapshot) or time-scattered observations of a small number of activity cascades. The method that we present is built on a Belief Propagation approximation, that has shown impressive accuracy in a wide variety of relevant cases, and is able to infer interactions in presence of incomplete time-series data by providing a detailed modeling of the posterior distribution of trajectories conditioned to the observations. Furthermore, we show by experiments that the information content of full cascades is relatively smaller than that of sparse observations or single snapshots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
