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Topology-dependent density optima for efficient simultaneous network exploration.

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Summary
This summary is machine-generated.

Optimizing parallel random searches in networks involves balancing searcher density against competition. We introduce searcher-averaged parallel cover time (APCT) to find optimal densities, showing bias can increase network capacity.

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Area of Science:

  • Network Science
  • Statistical Physics
  • Complex Systems

Background:

  • Random search processes in networks are fundamental to information dissemination and transport.
  • Cover time, the time to visit all nodes, is a key metric for search efficiency.
  • Concurrent searches often compete, raising questions about optimal parallelism.

Purpose of the Study:

  • To quantify the trade-off between searcher density and competition in networked environments.
  • To introduce a new metric, searcher-averaged parallel cover time (APCT), for optimizing parallel search.
  • To investigate the impact of nonequilibrium conditions on searcher density optima.

Main Methods:

  • Development and application of the searcher-averaged parallel cover time (APCT) metric.
  • Analysis of the networked symmetric exclusion process.
  • Utilizing spectral gap properties to predict optimal searcher densities.
  • Introducing bias to model nonequilibrium processes.

Main Results:

  • The APCT is optimized at a specific searcher density, predictable by the spectral gap.
  • Nonequilibrium processes, achieved through bias, significantly increase the optimal searcher density.
  • Demonstrated economies of scale in parallel network searches.

Conclusions:

  • Optimal parallel search strategies depend on network topology and searcher density.
  • Biased or nonequilibrium processes can enhance network capacity for parallel searches.
  • Findings suggest hybrid serial-parallel search strategies for efficient information gathering in real-world networks.