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Optimizing network small-world properties involves balancing average clustering coefficient (ACC) and average path length (APL). A new multiobjective evolutionary algorithm efficiently achieves this, offering insights into network structure optimization.

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Area of Science:

  • Network Science
  • Computational Social Science
  • Optimization Theory

Background:

  • The small-world effect is crucial in network science, impacting various applications.
  • Optimizing small-world properties, specifically average clustering coefficient (ACC) and average path length (APL), is a significant research focus.

Purpose of the Study:

  • To model the optimization of small-world properties as a multiobjective problem.
  • To develop and evaluate a novel method for simultaneously optimizing ACC and APL.

Main Methods:

  • Formulating small-world property optimization as a multiobjective problem.
  • Employing a multiobjective evolutionary algorithm with decomposition (MOEA/D).
  • Analyzing optimization results concerning network community structure.

Main Results:

  • The proposed MOEA/D method efficiently solves the small-world optimization problem.
  • The method generates a uniform distribution of solutions on the Pareto-optimal front.
  • Adding edges within communities enhances ACC, while inter-community edges reduce APL.

Conclusions:

  • The developed method effectively optimizes small-world network properties.
  • Network community structure significantly influences optimization outcomes, particularly when not saturated with triangles.
  • Strategies for optimizing ACC and APL differ based on edge placement relative to community structures.