2020/08/31 by Jean-Gabriel Young, George T. Cantwell, M. E. J. Newman · 70 citations
Computer Science · Mathematics · Neuroscience · Physics and Astronomy · Psychology · #Algorithm #Artificial intelligence #Bayesian network #Bayesian probability #Code (set theory) #Complex Network Analysis Techniques #Computer science #Data mining #Functional Brain Connectivity Studies #Inference #Machine learning #Mental Health Research Topics #Theoretical computer science #cs.SI #physics.soc-ph #stat.AP
paper · pdf · doi:10.1093/comnet/cnaa046
published in Journal of Complex Networks 8(6) (Oxford University Press) · 16 pages, 7 figures
openalex publication_date 2020/11/13 · arxiv created 2021/03/09 · arxiv updated 2021/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract 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.