By interpreting a temporal network as a trajectory of a latent graph
dynamical system, here we introduce the concept of dynamical instabil-
ity of a temporal network, and accordingly derive a measure to estimate
the network Maximum Lyapunov Exponent (nMLE) of a temporal net-
work trajectory. By building and extending classical algorithmic methods
from nonlinear time series analysis into the network realm, we show
how to quantify sensitive dependence on initial conditions and practi-
cally estimate the nMLE directly from single network trajectories. We
validate our method for a range of synthetic generative network models
and discuss its applicability in formulating and answering new questions
on the chaotic nature of interacting systems in different disciplines.
Presential in the IFISC seminar room, Zoom stream at https://zoom.us/j/98286706234?pwd=bm1JUFVYcTJkaVl1VU55L0FiWDRIUT09
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