Evolutionary graph theory

Paulo Shakarian, Abhinav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo

Research output: Chapter in Book/Report/Conference proceedingChapter

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 languageEnglish (US)
Title of host publicationSpringerBriefs in Computer Science
PublisherSpringer
Pages75-91
Number of pages17
Edition9783319231044
DOIs
StatePublished - Jan 1 2015

Publication series

NameSpringerBriefs in Computer Science
Number9783319231044
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)

Fingerprint Dive into the research topics of 'Evolutionary graph theory'. Together they form a unique fingerprint.

  • 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