Evolutionary dynamics on graphs Export

@article{citeulike:80418, abstract = {Evolutionary dynamics have been traditionally studied in the context of homogeneous or spatially extended populations1, 2, 3, 4. Here we generalize population structure by arranging individuals on a graph. Each vertex represents an individual. The weighted edges denote reproductive rates which govern how often individuals place offspring into adjacent vertices. The homogeneous population, described by the Moran process3, is the special case of a fully connected graph with evenly weighted edges. Spatial structures are described by graphs where vertices are connected with their nearest neighbours. We also explore evolution on random and scale-free networks5, 6, 7. We determine the fixation probability of mutants, and characterize those graphs for which fixation behaviour is identical to that of a homogeneous population7. Furthermore, some graphs act as suppressors and others as amplifiers of selection. It is even possible to find graphs that guarantee the fixation of any advantageous mutant. We also study frequency-dependent selection and show that the outcome of evolutionary games can depend entirely on the structure of the underlying graph. Evolutionary graph theory has many fascinating applications ranging from ecology to multi-cellular organization and economics.}, author = {Lieberman, Erez and Hauert, Christoph and Nowak, Martin A.}, citeulike-article-id = {80418}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/nature03204}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/nature03204}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/15662424}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=15662424}, doi = {10.1038/nature03204}, issn = {0028-0836}, journal = {Nature}, keywords = {evolutionary\_game\_theory, modeling, network, networks, small\_world, social\_network}, month = {January}, number = {7023}, pages = {312--316}, posted-at = {2006-04-05 23:54:34}, priority = {2}, publisher = {Nature Publishing Group}, title = {Evolutionary dynamics on graphs}, url = {http://dx.doi.org/10.1038/nature03204}, volume = {433}, year = {2005} }

TY - JOUR ID - citeulike:80418 L3 - citeulike-article-id:80418 N2 - Evolutionary dynamics have been traditionally studied in the context of homogeneous or spatially extended populations1, 2, 3, 4. Here we generalize population structure by arranging individuals on a graph. Each vertex represents an individual. The weighted edges denote reproductive rates which govern how often individuals place offspring into adjacent vertices. The homogeneous population, described by the Moran process3, is the special case of a fully connected graph with evenly weighted edges. Spatial structures are described by graphs where vertices are connected with their nearest neighbours. We also explore evolution on random and scale-free networks5, 6, 7. We determine the fixation probability of mutants, and characterize those graphs for which fixation behaviour is identical to that of a homogeneous population7. Furthermore, some graphs act as suppressors and others as amplifiers of selection. It is even possible to find graphs that guarantee the fixation of any advantageous mutant. We also study frequency-dependent selection and show that the outcome of evolutionary games can depend entirely on the structure of the underlying graph. Evolutionary graph theory has many fascinating applications ranging from ecology to multi-cellular organization and economics. IS - 7023 JF - Nature SN - 0028-0836 EP - 316 TI - Evolutionary dynamics on graphs PB - Nature Publishing Group VL - 433 SP - 312 KW - evolutionary_game_theory KW - modeling KW - network KW - networks KW - small_world KW - social_network AU - Lieberman, Erez AU - Hauert, Christoph AU - Nowak, Martin PY - 2005/01/20/ UR - http://dx.doi.org/10.1038/nature03204 ER -