Braess’s Paradox Analog in Physical Networks of Optimal Exploration

In stochastic exploration of geometrically embedded graphs, intuition suggests that providing a shortcut between a pair of nodes reduces the mean first passage time of the entire graph. Counterintuitively, we find a Braess's paradox analog. For regular diffusion, shortcuts can worsen the overall search efficiency of the network, although they bridge topologically distant nodes. We propose an optimization scheme under which each edge adapts its conductivity to minimize the graph's search time. The optimization reveals a relationship between the structure and diffusion exponent and a crossover from dense to sparse graphs as the exponent increases.

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Publication Details

Journal
Physical Review Letters
Published
2024-08-09
DOI
https://doi.org/10.1103/physrevlett.133.067401
Citations
3
Primary Topic
Diffusion and Search Dynamics
Type
article
Field-Weighted Citation Impact
0.70

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article

Braess’s Paradox Analog in Physical Networks of Optimal Exploration

G. J. Gounaris, Eleni Katifori
3 citations
Physical Review Letters
Diffusion and Search Dynamics
0.70
article

Braess’s Paradox Analog in Physical Networks of Optimal Exploration

G. J. Gounaris, Eleni Katifori
article en
3 citations

Abstract

In stochastic exploration of geometrically embedded graphs, intuition suggests that providing a shortcut between a pair of nodes reduces the mean first passage time of the entire graph. Counterintuitively, we find a Braess's paradox analog. For regular diffusion, shortcuts can worsen the overall search efficiency of the network, although they bridge topologically distant nodes. We propose an optimization scheme under which each edge adapts its conductivity to minimize the graph's search time. The optimization reveals a relationship between the structure and diffusion exponent and a crossover from dense to sparse graphs as the exponent increases.

Physical Review LettersVol. 133(6)
California University of Pennsylvania (US), Flatiron Health (United States) (US), Flatiron Institute (US), University of Pennsylvania (US)
National Science Foundation, Simons Foundation
Openalex Percentile: Top 30%
Diffusion and Search Dynamics
0.70
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