Seed Selection in the Heterogeneous Moran Process

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Seed Selection in the Heterogeneous Moran Process

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Article name in the collection

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

ISBN

978-1-956792-04-1

ISSN

1045-0823

e-ISSN

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Number of pages

9

Pages from-to

2297-2305

Publisher name

International Joint Conferences on Artificial Intelligence Organization

Place of publication

Neuveden

Event location

Jeju

Event date

Aug 3, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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