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Related Experiment Video

Updated: May 8, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Exact controllability of complex networks.

Zhengzhong Yuan¹, Chen Zhao, Zengru Di

¹School of Systems Science, Beijing Normal University, Beijing 100875, China.

Nature Communications

|September 13, 2013

Summary

This summary is machine-generated.

We present a new method to control complex networks, even those with weighted links. This framework identifies essential driver nodes for network control and assesses controllability in various network types.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Published on: December 7, 2021

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Area of Science:

Network Science
Control Theory
Complex Systems

Background:

Controlling complex networks is crucial in science and engineering.
Existing structural controllability theory lacks a framework for undirected networks, especially with link weights.

Purpose of the Study:

To introduce an exact controllability paradigm for undirected complex networks with arbitrary structures and link weights.
To identify the minimum set of driver nodes required for full network control.

Main Methods:

Developed an exact controllability paradigm based on maximum multiplicity.
The framework reproduces structural controllability for directed networks using structural matrices.
Applied the framework to analyze controllability in numerous real and model networks.

Main Results:

Identified the minimum set of driver nodes for controlling networks with diverse structures and weights.
Found that dense networks with uniform link weights are challenging to control.
Developed an efficient tool for assessing the controllability of large sparse and dense networks.

Conclusions:

The exact controllability framework provides a comprehensive understanding of network properties' impact on control.
This work advances the fundamental problem of controlling complex systems.