### Abstract

Evolutionary graph theory (EGT), studies the ability of a mutant gene to overtake a finite structured population. In this chapter, we describe the original framework for EGT and the major work that has followed it. Here, we will study the calculation of the “fixation probability”—the probability of a mutant taking over a population and focuses on game-theoretic applications. We look at varying topics such as alternate evolutionary dynamics, time to fixation, special topological cases, and game theoretic results.

Original language | English (US) |
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Title of host publication | SpringerBriefs in Computer Science |

Publisher | Springer |

Pages | 75-91 |

Number of pages | 17 |

Edition | 9783319231044 |

DOIs | |

State | Published - Jan 1 2015 |

### Publication series

Name | SpringerBriefs in Computer Science |
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Number | 9783319231044 |

ISSN (Print) | 2191-5768 |

ISSN (Electronic) | 2191-5776 |

### Keywords

- Evolutionary stability
- Large graph
- Payoff matrix
- Regular graph
- Undirected graph

### ASJC Scopus subject areas

- Computer Science(all)

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## Cite this

Shakarian, P., Bhatnagar, A., Aleali, A., Shaabani, E., & Guo, R. (2015). Evolutionary graph theory. In

*SpringerBriefs in Computer Science*(9783319231044 ed., pp. 75-91). (SpringerBriefs in Computer Science; No. 9783319231044). Springer. https://doi.org/10.1007/978-3-319-23105-1_6