Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks.

Proceedings of the National Academy of Sciences of the United States of America
Shuo WangJr-Shin Li

Abstract

Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity a...Continue Reading

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Citations

Oct 28, 2019·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·Michael SebekIstván Z Kiss
May 28, 2020·Scientific Reports·Walter BomelaJr-Shin Li
Aug 24, 2021·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Oliver SiehlerGuy Bloch
Sep 1, 2021·Bioinformatics·Jonathan TylerJaeKyoung Kim

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ICON

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