title: Solving the inverse problem of noise-driven dynamic networks.
abstract: Nowadays, massive amounts of data are available for analysis in natural and social systems and the tasks to depict system structures from the data, i.e., the inverse problems, become one of the central issues in wide interdisciplinary fields. In this paper, we study the inverse problem of dynamic complex networks driven by white noise. A simple and universal inference formula of double correlation matrices and noise-decorrelation (DCMND) method is derived analytically, and numerical simulations confirm that the DCMND method can accurately depict both network structures and noise correlations by using available output data only. This inference performance has never been regarded possible by theoretical derivation, numerical computation, and experimental design.
authors: Department of Physics, Beijing Normal University, Beijing 100875, China.
authors: Zhaoyang
authors: Z
authors: Zhang
authors: Department of Physics, Beijing Normal University, Beijing 100875, China.
authors: Zhigang
authors: Z
authors: Zheng
authors: State Key Laboratory of Cognitive Neuroscience and Learning and International Digital Group (IDG)/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China and Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors: Haijing
authors: H
authors: Niu
authors: State Key Laboratory of Cognitive Neuroscience and Learning and International Digital Group (IDG)/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China and Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors: Yuanyuan
authors: Y
authors: Mi
authors: State Key Laboratory of Cognitive Neuroscience and Learning and International Digital Group (IDG)/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China and Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors: Si
authors: S
authors: Wu
authors: Department of Physics, Beijing Normal University, Beijing 100875, China.
authors: Gang
authors: G
authors: Hu
mesh_terms: D000465:Algorithms; D016014:Linear Models; D008962:Models, Theoretical