TY - CHAP
T1 - Evolutionary graph theory
AU - Shakarian, Paulo
AU - Bhatnagar, Abhinav
AU - Aleali, Ashkan
AU - Shaabani, Elham
AU - Guo, Ruocheng
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Evolutionary stability
KW - Large graph
KW - Payoff matrix
KW - Regular graph
KW - Undirected graph
UR - https://www.scopus.com/pages/publications/85044943594
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85044943594&origin=recordpage
U2 - 10.1007/978-3-319-23105-1_6
DO - 10.1007/978-3-319-23105-1_6
M3 - Chapter in research book/monograph/textbook (Author)
SN - 9783319231044
T3 - SpringerBriefs in Computer Science
SP - 75
EP - 91
BT - Diffusion in Social Networks
PB - Springer
ER -