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Smart random walkers: the cost of knowing the path
Juan I Perotti1, Orlando V Billoni
1Facultad de Matemática, Astronomía y Física, Universidad Nacional de Córdoba and Instituto de Física Enrique Gaviola, IFEG-CONICET, Ciudad Universitaria, 5000 Córdoba, Argentina. perotti@famaf.unc.edu.ar
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 26, 2012
Summary
This study introduces a penalization rule to efficiently target signals in networks. Optimal signal targeting depends on network topology, with minimal penalization significantly reducing navigation time.
Area of Science:
- Network science
- Information theory
- Computational physics
Background:
- Signal targeting in complex networks is crucial for various applications.
- Quantifying the cost of targeting signals is an ongoing challenge.
- Entropy information measurements offer a framework for cost analysis.
Purpose of the Study:
- To develop a method for efficient signal targeting in networks.
- To quantify the cost of targeting using entropy information.
- To investigate the impact of network topology on targeting efficiency.
Main Methods:
- Introduction of a novel penalization rule to restrict long paths.
- Continuous adjustment from random walkers to target-biased walkers.
- Analysis of network topology to determine optimal penalization levels.
Main Results:
- A penalization scheme effectively focuses signals towards the target.
- Optimal penalization is strongly influenced by network topology.
- Even small amounts of penalization considerably reduce typical walk lengths.
Conclusions:
- Networks can be navigated efficiently using restricted information.
- The proposed penalization method offers a practical approach to signal targeting.
- Understanding network topology is key to optimizing targeting strategies.
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