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Random Walks on Networks with Centrality-Based Stochastic Resetting.

Kiril Zelenkovski1, Trifce Sandev1,2,3, Ralf Metzler3,4

  • 1Research Center for Computer Science and Information Technologies, Macedonian Academy of Sciences and Arts, Bul. Krste Misirkov 2, 1000 Skopje, Macedonia.

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Summary
This summary is machine-generated.

We developed a new random walk method using centrality measures to find targets faster in complex networks. This approach improves search efficiency, especially in real-world directed networks.

Keywords:
complex networksnode centralityrandom walksstochastic resetting

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Area of Science:

  • Complex network analysis
  • Stochastic processes
  • Information theory

Background:

  • Efficiently exploring complex networks is crucial for various applications.
  • Traditional random walk methods can be slow and inefficient in large networks.
  • Stochastic resetting offers a way to enhance search efficiency by periodically resetting the walker's position.

Purpose of the Study:

  • To introduce a novel strategy for stochastic resetting in complex networks.
  • To utilize node centrality measures for selecting optimal resetting sites.
  • To evaluate the impact of this centrality-focused resetting on search performance.

Main Methods:

  • Developing a random walk model with resetting to the network's geometric center.
  • Employing Markov chain theory to calculate the Global Mean First Passage Time (GMFPT).
  • Analyzing various network topologies, including generic, real-life directed, and undirected scale-free networks.

Main Results:

  • Centrality-focused resetting significantly improves search efficiency in real-world directed networks compared to undirected ones.
  • Resetting to the geometric center minimizes average travel time in real networks.
  • The effectiveness of stochastic resetting depends on network properties like sparsity, diameter, and average node degree.

Conclusions:

  • The proposed random walk with centrality-based stochastic resetting offers a more efficient method for target searching in complex networks.
  • This approach is particularly beneficial for directed networks and sparse, tree-like undirected networks.
  • The findings provide insights into optimizing search strategies in diverse network structures.