Data based identification and prediction of nonlinear and complex dynamical systems
The problem of reconstructing nonlinear and complex dynamical systems from measured
data or time series is central to many scientific disciplines including physical, biological …
data or time series is central to many scientific disciplines including physical, biological …
Causal connectivity measures for pulse-output network reconstruction: Analysis and applications
ZK Tian, K Chen, S Li… - Proceedings of the …, 2024 - National Acad Sciences
The causal connectivity of a network is often inferred to understand network function. It is
arguably acknowledged that the inferred causal connectivity relies on the causality measure …
arguably acknowledged that the inferred causal connectivity relies on the causality measure …
Machine learning link inference of noisy delay-coupled networks with optoelectronic experimental tests
We devise a machine learning technique to solve the general problem of inferring network
links that have time delays using only time series data of the network nodal states. This task …
links that have time delays using only time series data of the network nodal states. This task …
Causal inference in nonlinear systems: Granger causality versus time-delayed mutual information
S Li, Y Xiao, D Zhou, D Cai - Physical Review E, 2018 - APS
The Granger causality (GC) analysis has been extensively applied to infer causal
interactions in dynamical systems arising from economy and finance, physics …
interactions in dynamical systems arising from economy and finance, physics …
Machine learning prediction of network dynamics with privacy protection
Predicting network dynamics based on data, a problem with broad applications, has been
studied extensively in the past, but most existing approaches assume that the complete set …
studied extensively in the past, but most existing approaches assume that the complete set …
Data based reconstruction of duplex networks
It has been recognized that many complex dynamical systems in the real world require a
description in terms of multiplex networks, where a set of common, mutually connected …
description in terms of multiplex networks, where a set of common, mutually connected …
Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems
Reconstruction of anatomical connectivity from measured dynamical activities of coupled
neurons is one of the fundamental issues in the understanding of structure-function …
neurons is one of the fundamental issues in the understanding of structure-function …
Statistical inference approach to structural reconstruction of complex networks from binary time series
Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of
previous works, to fully reconstruct the network structure from observed binary data remains …
previous works, to fully reconstruct the network structure from observed binary data remains …
Data-based reconstruction of complex geospatial networks, nodal positioning and detection of hidden nodes
Given a complex geospatial network with nodes distributed in a two-dimensional region of
physical space, can the locations of the nodes be determined and their connection patterns …
physical space, can the locations of the nodes be determined and their connection patterns …
Maximum entropy principle underlies wiring length distribution in brain networks
A brain network comprises a substantial amount of short-range connections with an
admixture of long-range connections. The portion of long-range connections in brain …
admixture of long-range connections. The portion of long-range connections in brain …