Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition
Journal:
arXiv
Published Date:
Dec 19, 2024
Abstract
Early-warning signals of delicate design are always used to predict critical
transitions in complex systems, which makes it possible to render the systems
far away from the catastrophic state by introducing timely interventions.
Traditional signals including the dynamical network biomarker (DNB), based on
statistical properties such as variance and autocorrelation of nodal dynamics,
overlook directional interactions and thus have limitations in capturing
underlying mechanisms and simultaneously sustaining robustness against noise
perturbations. This paper therefore introduces a framework of causal network
markers (CNMs) by incorporating causality indicators, which reflect the
directional influence between variables. Actually, to detect and identify the
tipping points ahead of critical transition, two markers are designed: CNM-GC
for linear causality and CNM-TE for non-linear causality, as well as a
functional representation of different causality indicators and a clustering
technique to verify the system's dominant group. Through demonstrations using
benchmark models and real-world datasets of epileptic seizure, the framework of
CNMs shows higher predictive power and accuracy than the traditional DNB
indicator. It is believed that, due to the versatility and scalability, the
CNMs are suitable for comprehensively evaluating the systems. The most possible
direction for application includes the identification of tipping points in
clinical disease.
