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Updated: Mar 9, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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Trade-offs between driving nodes and time-to-control in complex networks.

Sérgio Pequito1, Victor M Preciado1, Albert-László Barabási2,3,4

  • 1Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.

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Summary

Researchers developed the actuation spectrum to analyze trade-offs in controlling complex networks. This reveals that few nodes are needed to steer networks, but synthetic models may not accurately reflect real-world controllability.

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Area of Science:

  • Network Science
  • Control Theory
  • Systems Engineering

Background:

  • Complex networks require control to reach desired states.
  • Understanding the trade-offs between control effort (nodes) and time is crucial for practical applications.

Purpose of the Study:

  • Introduce and utilize the "actuation spectrum" to quantify trade-offs in network control.
  • Investigate the relationship between the number of driving nodes and the time needed to steer complex networks.

Main Methods:

  • Developed the concept of the actuation spectrum.
  • Applied empirical studies to analyze actuation spectra in various complex networks.
  • Compared actuation spectra of real-world networks with synthetic models.

Main Results:

  • Identified that a small fraction of driving nodes is sufficient to steer many complex networks within a limited time.
  • Demonstrated significant differences between the actuation spectra of real networks and common synthetic models.
  • Highlighted the limitations of current synthetic network models in replicating controllability properties.

Conclusions:

  • The actuation spectrum is a valuable tool for understanding network controllability trade-offs.
  • Current synthetic network models may not accurately represent the control dynamics of real-world systems.
  • There is a need for improved synthetic network models that capture essential controllability features.