Network reconstruction from noise-driving dynamic data in complex networks

Abstract

Most complex social, biological, and technological systems can be described by dynamic networks. Reconstructing the structure of complex networks from mea-surable data of some or all nodes is a challenge in many branches of science. External influences are always present and act as noises to the networks of inter-est, and various difficulties extensively appear in the reconstruction of real-world networks: such as complexity of network structures; strong nonlinearity of net-work dynamics; diverse and unknown impacts from the interiors of nodes and externals of networks, i.e., noises; many hidden nodes of which data are not measurable in networks; and different time delays of interactions. Partial or all the above mentioned difficulties are present in reconstruction of noise-driving dynamic network. Different methods are proposed to solve these difficulties, including variable-variable correlations, velocity-variable correlations, high-order correlation, time-lagged covariance of data measurements taken at different times, and so on. This review shows partial developments of a special topic in this wide field. Moreover, we expect that all the methods in this review can be applied to the reconstruction of many realistic dynamic networks.

Keywords

network reconstruction / dynamic networks / complex systems

RIGHTS & PERMISSIONS

Higher Education Press 2026

Just Accepted

This article has successfully passed peer review and final editorial review, and will soon enter typesetting, proofreading and other publishing processes. The currently displayed version is the accepted final manuscript. The officially published version will be updated with format, DOI and citation information upon launch. We recommend that you pay attention to subsequent journal notifications and preferentially cite the officially published version. Thank you for your support and cooperation.