Dynamic structure of networks updated according to simple, local rules.
While most studies of deterministic network growth have been of one- or two-case models, here a more diverse and comprehensive method of deterministic network evolution is presented. The range of observed behavior is classified and the underlying causes of the various types of growth are investigated. The potential for prediction of the different types of network growth is also examined. It is discovered that a wide variety of behavior can be produced by a simple evolutionary setup and that the networks resulting from this method of evolution warrant further study.
Morrow
Kate L
KL
Department of Mathematics and Statistics, University of Vermont, Burlington, Vermont 05401, USA.
Rowland
Todd
T
Danforth
Christopher M
CM