Seed Selection in the Heterogeneous Moran Process

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10486574" target="_blank" >RIV/00216208:11320/24:10486574 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.24963/ijcai.2024/254" target="_blank" >https://doi.org/10.24963/ijcai.2024/254</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24963/ijcai.2024/254" target="_blank" >10.24963/ijcai.2024/254</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Seed Selection in the Heterogeneous Moran Process

Popis výsledku v původním jazyce

The Moran process is a classic stochastic process that models the rise and takeover of novel traits in network-structured populations. In biological terms, a set of mutants, each with fitness mELEMENT OF(0,oo) invade a population of residents with fitness 1. Each agent reproduces at a rate proportional to its fitness and each offspring replaces a random network neighbor. The process ends when the mutants either fixate (take over the whole population) or go extinct. The fixation probability measures the success of the invasion. To account for environmental heterogeneity, we study a generalization of the Standard process, called the Heterogeneous Moran process. Here, the fitness of each agent is determined both by its type (resident/mutant) and the node it occupies. We study the natural optimization problem of seed selection: given a budget k, which k agents should initiate the mutant invasion to maximize the fixation probability? We show that the problem is strongly inapproximable: it is NP-hard to distinguish between maximum fixation probability 0 and 1. We then focus on mutant-biased networks, where each node exhibits at least as large mutant fitness as resident fitness. We show that the problem remains NP-hard, but the fixation probability becomes submodular, and thus the optimization problem admits a greedy (1-1/e)-approximation. An experimental evaluation of the greedy algorithm along with various heuristics on real-world data sets corroborates our results.

Název v anglickém jazyce

Seed Selection in the Heterogeneous Moran Process

Popis výsledku anglicky

The Moran process is a classic stochastic process that models the rise and takeover of novel traits in network-structured populations. In biological terms, a set of mutants, each with fitness mELEMENT OF(0,oo) invade a population of residents with fitness 1. Each agent reproduces at a rate proportional to its fitness and each offspring replaces a random network neighbor. The process ends when the mutants either fixate (take over the whole population) or go extinct. The fixation probability measures the success of the invasion. To account for environmental heterogeneity, we study a generalization of the Standard process, called the Heterogeneous Moran process. Here, the fitness of each agent is determined both by its type (resident/mutant) and the node it occupies. We study the natural optimization problem of seed selection: given a budget k, which k agents should initiate the mutant invasion to maximize the fixation probability? We show that the problem is strongly inapproximable: it is NP-hard to distinguish between maximum fixation probability 0 and 1. We then focus on mutant-biased networks, where each node exhibits at least as large mutant fitness as resident fitness. We show that the problem remains NP-hard, but the fixation probability becomes submodular, and thus the optimization problem admits a greedy (1-1/e)-approximation. An experimental evaluation of the greedy algorithm along with various heuristics on real-world data sets corroborates our results.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence

ISBN

978-1-956792-04-1

ISSN

1045-0823

e-ISSN

—

Počet stran výsledku

9

Strana od-do

2297-2305

Název nakladatele

International Joint Conferences on Artificial Intelligence Organization

Místo vydání

Neuveden

Místo konání akce

Jeju

Datum konání akce

3. 8. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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