2020/12/31 by Giulio Cimini, Rossana Mastrandrea, Tiziano Squartini · 34 citations
Engineering · Mathematics · Physics and Astronomy · #Artificial intelligence #Complex Network Analysis Techniques #Complex network #Computer science #Data mining #Data science #Engineering #Field (mathematics) #Focus (optics) #Inference #Mathematics #Opinion Dynamics and Social Influence #Physics #Statistical inference #Subject (documents) #Task (project management) #Theoretical and Computational Physics #Theoretical computer science #World Wide Web #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1017/9781108771030
published in Cambridge University Press eBooks (Cambridge University Press) · 107 pages, 25 figures
arxiv created 2021/01/29 · openalex publication_date 2021/08/10 · arxiv updated 2021/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, the authors focus on the inference methods rooted in statistical physics and information theory. The discussion is organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections.