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Optimizing target nodes selection for the control energy of directed complex networks.

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This study introduces a new method to minimize network control energy by optimizing target node selection. The approach finds optimal target nodes, reducing energy needs significantly compared to existing strategies.

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Area of Science:

  • Network Science
  • Control Theory
  • Optimization

Background:

  • Controlling complex networks requires significant energy.
  • Existing methods focus on driver node placement or reducing controlled nodes.
  • Optimizing target node selection for energy reduction is underexplored.

Purpose of the Study:

  • To develop an iterative method for reducing network control energy.
  • To optimize target node selection using Stiefel manifold optimization.
  • To investigate the relationship between path distances and control energy.

Main Methods:

  • Iterative optimization using Stiefel manifold.
  • Derivation of matrix derivative gradients for search algorithms.
  • Simulation on diverse network topologies (elementary, random, scale-free, real-world).

Main Results:

  • Proposed method significantly reduces control energy.
  • Optimal control energy achieved when path distances from driver to target nodes are minimized.
  • Outperforms heuristic target node selection strategies by orders of magnitude.

Conclusions:

  • Iterative Stiefel manifold optimization is effective for reducing control energy.
  • Minimizing path distances is key to optimal target node selection.
  • Applicable to areas like opinion network influence maximization.