10.4230/LIPIcs.MFCS.2017.61
Chatterjee, Krishnendu
Krishnendu
Chatterjee0000-0002-4561-241X
Ibsen-Jensen, Rasmus
Rasmus
Ibsen-Jensen
Nowak, Martin
Martin
Nowak
Faster Monte Carlo algorithms for fixation probability of the Moran process on undirected graphs
LIPIcs
Schloss Dagstuhl - Leibniz-Zentrum für Informatik
2017
2018-12-11T11:47:08Z
2019-08-02T12:38:56Z
conference
https://research-explorer.app.ist.ac.at/record/551
https://research-explorer.app.ist.ac.at/record/551.json
978-395977046-0
535077 bytes
application/pdf
Evolutionary graph theory studies the evolutionary dynamics in a population structure given as a connected graph. Each node of the graph represents an individual of the population, and edges determine how offspring are placed. We consider the classical birth-death Moran process where there are two types of individuals, namely, the residents with fitness 1 and mutants with fitness r. The fitness indicates the reproductive strength. The evolutionary dynamics happens as follows: in the initial step, in a population of all resident individuals a mutant is introduced, and then at each step, an individual is chosen proportional to the fitness of its type to reproduce, and the offspring replaces a neighbor uniformly at random. The process stops when all individuals are either residents or mutants. The probability that all individuals in the end are mutants is called the fixation probability, which is a key factor in the rate of evolution. We consider the problem of approximating the fixation probability. The class of algorithms that is extremely relevant for approximation of the fixation probabilities is the Monte-Carlo simulation of the process. Previous results present a polynomial-time Monte-Carlo algorithm for undirected graphs when r is given in unary. First, we present a simple modification: instead of simulating each step, we discard ineffective steps, where no node changes type (i.e., either residents replace residents, or mutants replace mutants). Using the above simple modification and our result that the number of effective steps is concentrated around the expected number of effective steps, we present faster polynomial-time Monte-Carlo algorithms for undirected graphs. Our algorithms are always at least a factor O(n2/ log n) faster as compared to the previous algorithms, where n is the number of nodes, and is polynomial even if r is given in binary. We also present lower bounds showing that the upper bound on the expected number of effective steps we present is asymptotically tight for undirected graphs.