20.500.12556/DKUM-90131 Ranking the invasions of cheaters in structured populations The identification of the most influential individuals in structured populations is an important research question, with many applications across the social and natural sciences. Here, we study this problem in evolutionary populations on static networks, where invading cheaters can lead to the collapse of cooperation. We propose six strategies to rank the invading cheaters and identify those which mostly facilitate the collapse of cooperation. We demonstrate that the type of successful rankings depend on the selection strength, the underlying game, and the network structure. We show that random ranking has generally little ability to successfully identify invading cheaters, especially for the stag-hunt game in scale-free networks and when the selection strength is strong. The ranking based on degree can successfully identify the most influential invaders when the selection strength is weak, while more structured rankings perform better at strong selection. Scale-free networks and strong selection are generally detrimental to the performance of the random ranking, but they are beneficial for the performance of structured rankings. Our research reveals how to identify the most influential invaders using statistical measures in structured communities, and it demonstrates how their success depends on population structure, selection strength, and on the underlying game dynamics. cooperation cheating network population evolution sodelovanje goljufija omrežje populacije evolucija true false true Nature Publishing Group Angleški jezik Slovenski jezik Znanstveno delo 2024-08-23 04:14:06 2025-01-07 12:30:11 2025-01-08 03:10:49 0000-00-00 00:00:00 2020 0 0 Str. 1-13 št. članka 2231 Letn. 10 2020 0000-00-00 Zaloznikova Objavljeno apc 2019-12-17 2020-01-24 2020-02-10 53 25113864 10.1038/s41598-020-59020-4 2045-2322 Yang-2020-Ranking_the_invasions_of_cheaters_in.pdf Yang-2020-Ranking_the_invasions_of_cheaters_in.pdf 1 06367E32F175ED79B7ADDA504BB84C95 8d48d2a38826a3cf1ac547ab9de7fff4ac247bc63c118db4cae0f5a95fc919ab 761f5478-ccea-11ef-8aab-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=187653 https://doi.org/10.1038/s41598-020-59020-4 0 a92de1ca-ccea-11ef-8aab-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=187654 Fakulteta za naravoslovje in matematiko 0 0 0