Bayesian inference of network structure from unreliable data
Bayesian inference of network structure from unreliable data
复制标题
DOI：
10.1093/comnet/cnaa046
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Jean-Gabriel Young;George T. Cantwell;Mark E. J. Newman
中科院分区：
文献类型：
--
作者：
Jean-Gabriel Young;George T. Cantwell;Mark E. J. Newman
大多数复杂网络的实证研究并没有返回直接的、无误差的网络结构测量结果。相反，它们通常依赖于间接测量，这些测量通常容易出错且不可靠。经验网络科学中的一个基本问题是如何在这样不可靠的数据下对网络结构做出尽可能好的估计。在这篇文章中，我们描述了一个完整的贝叶斯方法，用于从任何格式的观测数据中重建网络，即使数据包含大量的测量误差，并且该误差的性质和大小是未知的。该方法是通过教学案例研究，使用真实世界的例子网络，并专门定制，以允许简单，计算效率高的实施与最小的技术投入。实现该方法的计算机代码是公开可用的。
Most empirical studies of complex networks do not return direct, error-free measurements of network structure. Instead, they typically rely on indirect measurements that are often error prone and unreliable. A fundamental problem in empirical network science is how to make the best possible estimates of network structure given such unreliable data. In this article, we describe a fully Bayesian method for reconstructing networks from observational data in any format, even when the data contain substantial measurement error and when the nature and magnitude of that error is unknown. The method is introduced through pedagogical case studies using real-world example networks, and specifically tailored to allow straightforward, computationally efficient implementation with a minimum of technical input. Computer code implementing the method is publicly available.