Categorizing update mechanisms for graph-structured metapopulations

被引:12
|
作者
Yagoobi, Sedigheh [1 ]
Sharma, Nikhil [1 ]
Traulsen, Arne [1 ]
机构
[1] Max Planck Inst Evolutionary Biol, Dept Evolutionary Theory, August Thienemann Str 2, D-24306 Plon, Germany
关键词
evolutionary graph theory; graph-structured metapopulation; network-structured metapopulation; update mechanism; FIXATION PROBABILITY; EVOLUTIONARY DYNAMICS; POPULATION; MUTANT; COOPERATION; NETWORKS; CANCER; GENES; GAMES; MODEL;
D O I
10.1098/rsif.2022.0769
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The structure of a population strongly influences its evolutionary dynamics. In various settings ranging from biology to social systems, individuals tend to interact more often with those present in their proximity and rarely with those far away. A common approach to model the structure of a population is evolutionary graph theory. In this framework, each graph node is occupied by a reproducing individual. The links connect these individuals to their neighbours. The offspring can be placed on neighbouring nodes, replacing the neighbours-or the progeny of its neighbours can replace a node during the course of ongoing evolutionary dynamics. Extending this theory by replacing single individuals with subpopulations at nodes yields a graph-structured metapopulation. The dynamics between the different local subpopulations is set by an update mechanism. There are many such update mechanisms. Here, we classify update mechanisms for structured metapopulations, which allows to find commonalities between past work and illustrate directions for further research and current gaps of investigation.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Graph-Structured Visual Imitation
    Sieb, Maximilian
    Xian, Zhou
    Huang, Audrey
    Kroemer, Oliver
    Fragkiadaki, Katerina
    [J]. CONFERENCE ON ROBOT LEARNING, VOL 100, 2019, 100
  • [2] The case for graph-structured representations
    Sanders, KE
    Kettler, BP
    Hendler, JA
    [J]. CASE-BASED REASONING RESEARCH AND DEVELOPMENT, 1997, 1266 : 245 - 254
  • [3] Querying graph-structured data
    Cheng, Jiefeng
    Yu, Jeffrey Xu
    [J]. 2007 IFIP INTERNATIONAL CONFERENCE ON NETWORK AND PARALLEL COMPUTING WORKSHOPS, PROCEEDINGS, 2007, : 23 - 27
  • [4] Reproductive value in graph-structured populations
    Maciejewski, Wes
    [J]. JOURNAL OF THEORETICAL BIOLOGY, 2014, 340 : 285 - 293
  • [5] Nested graph-structured representations for cases
    Macedo, L
    Cardoso, A
    [J]. ADVANCES IN CASE-BASED REASONING, 1998, 1488 : 1 - 12
  • [6] A Graph-Structured Dataset for Wikipedia Research
    Aspert, Nicolas
    Miz, Volodymyr
    Ricaud, Benjamin
    Vandergheynst, Pierre
    [J]. COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), 2019, : 1188 - 1193
  • [7] Inference and Search on Graph-Structured Spaces
    Wu C.M.
    Schulz E.
    Gershman S.J.
    [J]. Computational Brain & Behavior, 2021, 4 (2) : 125 - 147
  • [8] Graph-Structured Gaussian Processes for Transferable Graph Learning
    Wu, Jun
    Ainsworth, Elizabeth
    Leakey, Andrew
    Wang, Haixun
    He, Jingrui
    [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [9] Convolutional Kernel Networks for Graph-Structured Data
    Chen, Dexiong
    Jacob, Laurent
    Mairal, Julien
    [J]. 25TH AMERICAS CONFERENCE ON INFORMATION SYSTEMS (AMCIS 2019), 2019,
  • [10] Unimodal Thompson Sampling for Graph-Structured Arms
    Paladino, Stefano
    Trovo, Francesco
    Restelli, Marcello
    Gatti, Nicola
    [J]. THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2017, : 2457 - 2463