利用自然多样性重构传播网络并识别隐藏源
Reconstructing propagation networks with natural diversity and identifying hidden sources
摘要：
我们从数据中揭示复杂网络结构和动态的能力是理解和控制复杂系统中集体动态的基础。尽管最近在这一领域取得了进展，从有限的时间序列重建网络的随机动力学过程仍然是一个突出的问题。在这里，我们开发了一个基于压缩感知的框架，以重建随机传播动态发生的复杂网络。我们将该方法应用于大量的模型和真实网络，发现可以从少量的极化（二进制）数据中实现非均匀相互作用的完全重建，这是压缩感知的一个优点。此外，我们证明了一个隐藏的源，触发的传播过程，但外部无法访问，可以确定和定位的直接传播路径的情况下，具有很高的信心。因此，我们的方法建立了一个范例，跟踪和控制流行病入侵和信息扩散在复杂的网络系统。
英文摘要：
Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems.
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。
