The Moran Process on a Random Graph

ABSTRACT We study the fixation probability for two versions of the Moran process on the random graph at the threshold for connectivity. The Moran process models the spread of a mutant population in a network. Throughout the process, there are vertices of two types, mutants, and non‐mutants. Mutants have fitness and non‐mutants have fitness 1. The process starts with a unique individual mutant located at the vertex . In the Birth‐Death version of the process a random vertex is chosen proportionally to its fitness and then changes the type of a random neighbor to its own. The process continues until the set of mutants is empty or . In the Death‐Birth version, a uniform random vertex is chosen and then takes the type of a random neighbor, chosen according to fitness. The process again continues until the set of mutants is empty or . The fixation probability is the probability that the process ends with . We show that asymptotically correct estimates of the fixation probability depend only on the degree of and its neighbors. In some cases we can provide values for these estimates and in other places we can only provide non‐linear recurrences that could be used to compute values.

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Publication Details

Journal
Random Structures and Algorithms
Published
2025-04-02
DOI
https://doi.org/10.1002/rsa.70003
Primary Topic
Evolutionary Game Theory and Cooperation
Type
article
Field-Weighted Citation Impact
0.00

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article

The Moran Process on a Random Graph

Wesley Pegden, Alan Frieze
Random Structures and Algorithms
Evolutionary Game Theory and Cooperation
article

The Moran Process on a Random Graph

Wesley Pegden, Alan Frieze
article en

Abstract

ABSTRACT We study the fixation probability for two versions of the Moran process on the random graph at the threshold for connectivity. The Moran process models the spread of a mutant population in a network. Throughout the process, there are vertices of two types, mutants, and non‐mutants. Mutants have fitness and non‐mutants have fitness 1. The process starts with a unique individual mutant located at the vertex . In the Birth‐Death version of the process a random vertex is chosen proportionally to its fitness and then changes the type of a random neighbor to its own. The process continues until the set of mutants is empty or . In the Death‐Birth version, a uniform random vertex is chosen and then takes the type of a random neighbor, chosen according to fitness. The process again continues until the set of mutants is empty or . The fixation probability is the probability that the process ends with . We show that asymptotically correct estimates of the fixation probability depend only on the degree of and its neighbors. In some cases we can provide values for these estimates and in other places we can only provide non‐linear recurrences that could be used to compute values.

Random Structures and AlgorithmsVol. 66(3)
Carnegie Mellon University (US)
National Science Foundation of Sri Lanka
Openalex Percentile: Top 92%
Evolutionary Game Theory and Cooperation
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