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Structural controllability of general edge dynamics in complex network.

Shaopeng Pang1, Yue Zhou2, Xiang Ren3

  • 1School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China. pang_shao_peng@163.com.

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Summary
This summary is machine-generated.

This study introduces a new model for edge dynamics in complex networks, relaxing traditional coupling assumptions. It reveals unique driver nodes for edge controllability, influenced by network structure and coupling density.

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Area of Science:

  • Network Science
  • Complex Systems Theory
  • Information Theory

Background:

  • Dynamic processes on network edges are crucial for understanding real-world systems.
  • Traditional edge controllability models assume all adjacent edge states are coupled.
  • This study addresses limitations in existing edge controllability frameworks.

Purpose of the Study:

  • To propose a general edge dynamics model that relaxes the all-to-all coupling restriction.
  • To develop a theoretical framework for studying the structural controllability of general edge dynamics.
  • To analyze the factors influencing edge controllability in complex networks.

Main Methods:

  • Developed a general edge dynamics model.
  • Established a theoretical framework for structural controllability analysis.
  • Applied the framework to various model and real-world networks.

Main Results:

  • Identified a unique set of driver nodes for edge controllability, determined by local node information.
  • Established lower and upper bounds for edge controllability, dependent on coupling density.
  • Found that homogeneous and sparse networks exhibit a higher proportion of effective coupling, influenced by degree distribution.

Conclusions:

  • The proposed model offers a more general approach to edge controllability.
  • Network properties like coupling density and degree distribution significantly impact edge controllability.
  • Edge controllability is influenced by local node information and network topology.