Revision as of 08:07, 19 September 2019

Using Mathematica, Netlogo (the latter available also on the web)

= Netlogo simulations and their relation to MCSN

Check out in particular the following Netlogo web simulations:

preferential attachment network (model fame and fandom)
diffusion on a directed network (model flow of musical objects, like tapes, with no replication)
small worlds network (model distance in the real world, where a small number of hops gets you far)
growth of giant component (in a random network of size N with probability p of a link, the giant component is the largest component if it grows proportional to N. This turns out to happen as soon as the average degree is 1, i.e. N/2 links, so the probability is (N/2)/(N(N-1)/2) = 1/(N-1) . On the other hand we can let k increase.

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 Using [http://wolfram.com/ Mathematica], [https://ccl.northwestern.edu/netlogo/ Netlogo] (the latter available also [http://www.netlogoweb.org/launch#http://www.netlogoweb.org/assets/modelslib/Sample%20Models/Biology/Ants.nlogo on the web])
+== Netlogo simulations and their relation to MCSN =
 Check out in particular the following [http://www.netlogoweb.org/launch#http://www.netlogoweb.org/assets/modelslib/Sample%20Models/Biology/Evolution/Cooperation.nlogo Netlogo web] simulations:
 * [http://www.netlogoweb.org/launch#http://www.netlogoweb.org/assets/modelslib/Sample%20Models/Networks/Preferential%20Attachment.nlogo preferential attachment network] (model fame and fandom)
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 * [http://www.netlogoweb.org/launch#http://www.netlogoweb.org/assets/modelslib/Sample%20Models/Networks/Giant%20Component.nlogo growth of giant component] (in a random network of size N with probability p of a link, the giant component is the largest component if it grows proportional to N. This turns out to happen as soon as the average degree is 1, i.e. N/2 links, so the probability is (N/2)/(N(N-1)/2) = 1/(N-1) . On the other hand we can let k increase.
-=== Graphs ===
+=== Graphs, generally ===
 [http://ccl.northwestern.edu/netlogo/models/PreferentialAttachment Preferential attachment networks]