Tue, 21 May 2024

14:00 - 15:00
C4

Fixation probability and suppressors of natural selection on higher-order networks

Naoki Masuda
(The State University of New York at Buffalo)
Abstract

Population structure substantially affects evolutionary dynamics. Networks that promote the spreading of fitter mutants are called amplifiers of selection, and those that suppress the spreading of fitter mutants are called suppressors of selection. It has been discovered that most networks are amplifiers under the so-called birth-death updating combined with uniform initialization, which is a common condition. We discuss constant-selection evolutionary dynamics with binary node states (which is equivalent to the biased voter model with two opinions in statistical physics research community) on higher-order networks, i.e., hypergraphs, temporal networks, and multilayer networks. In contrast to the case of conventional networks, we show that a vast majority of these higher-order networks are suppressors of selection, which we show by random-walk and Martingale analyses as well as by numerical simulations. Our results suggest that the modeling framework for structured populations in addition to the specific network structure is an important determinant of evolutionary dynamics.
 

Tue, 08 Feb 2022

14:00 - 15:00
Virtual

FFTA: Spreading processes on metapopulation models with node2vec mobility

Lingqi Meng
(The State University of New York at Buffalo)
Abstract

A metapopulation model, composed of subpopulations and pairwise connections, is a particle-network framework for epidemic dynamics study. Individuals are well-mixed within each subpopulation and migrate from one subpopulation to another, obeying a given mobility rule. While different mobility rules in metapopulation models have been studied, few efforts have been made to compare the effects of simple (i.e., unbiased) random walks and more complex mobility rules. In this talk, we study susceptible-infectious-susceptible (SIS) dynamics in a metapopulation model, in which individuals obey a second-order parametric random-walk mobility rule called the node2vec. We transform the node2vec mobility rule to a first-order Markov chain whose state space is composed of the directed edges and then derive the epidemic threshold. We find that the epidemic threshold is larger for various networks when individuals avoid frequent backtracking or visiting a neighbor of the previously visited subpopulation than when individuals obey the simple random walk. The amount of change in the epidemic threshold induced by the node2vec mobility is generally not as significant as, but is sometimes comparable with, the one induced by the change in the diffusion rate for individuals.

arXiv links: https://arxiv.org/abs/2006.04904 and https://arxiv.org/abs/2106.08080

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