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Ranking competitors using degree-neutralized random walks.

Seungkyu Shin1, Sebastian E Ahnert2, Juyong Park1

  • 1Graduate School of Culture Technology, Korea Advanced Institute of Science & Technology, Daejeon, Republic of Korea.

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Summary
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This study introduces a novel network-based ranking method using random walks to determine dominance hierarchies in competitive systems. The approach accurately ranks competitors by minimizing bias, outperforming existing methods in sparse networks.

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Area of Science:

  • Complex Systems
  • Network Science
  • Evolutionary Dynamics

Background:

  • Competition is fundamental to biological, social, and technological systems.
  • Determining dominance hierarchies is crucial for understanding system evolution and component success.
  • Existing ranking methods can be influenced by node degree, introducing bias.

Purpose of the Study:

  • To develop a robust ranking method for competitive systems.
  • To establish a dominance hierarchy measure free from degree-induced bias.
  • To assess the performance of the proposed method against existing techniques.

Main Methods:

  • A random walk model on a weighted directed network representing competitors and competitions.
  • Utilizing edge weights and node degrees to define a gradient guiding the random walker.
  • Interpreting steady-state occupancy as a measure of node strength or weakness.

Main Results:

  • The proposed method provides a bias-free measure of competitor strength or weakness.
  • The ranking method demonstrates improved accuracy in sparse networks.
  • Performance evaluation shows superiority over baseline methods like win-loss differential.

Conclusions:

  • The random walk-based ranking method offers a reliable approach for analyzing competitive systems.
  • This technique enhances the understanding of evolutionary dynamics by accurately assessing dominance hierarchies.
  • The method's effectiveness in sparse networks has significant implications for real-world applications.