Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks

Shuo Wang, Erik D. Herzog, István Z. Kiss, William J. Schwartz, Guy Bloch, Michael Sebek, Daniel Granados-Fuentes, Liang Wang, Jr Shin Li*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


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 among mice over weeks.

Original languageAmerican English
Pages (from-to)9300-9305
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number37
StatePublished - 11 Sep 2018

Bibliographical note

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© 2018 National Academy of Sciences. All Rights Reserved.


  • Circadian rhythms
  • Complex networks
  • Dynamic topology
  • Network inference
  • Social synchronization


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