Evolutionary graph theory

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

Research output: Chapter in Book/Report/Conference proceedingChapter


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
Number of pages17
StatePublished - Jan 1 2015

Publication series

NameSpringerBriefs in Computer Science
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776


  • 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