Community Discovery Methods for Complex Networks

24 Jul 2016 22:35

Given: a network, especially a large one, directed or not, weighted or not. Desired: a sensible decomposition of the graph into sub-graphs, where in some reasonable sense the nodes in each sub-graph have more to do with each other than with outsiders, i.e., form communities. This is also called "module detection".

This seems like a really useful idea to apply to problems I'm interested in, in neural synchronization; also a place where there could stand to be more interchange between statisticians and complex-network-wallahs. [That was written many years ago; I think that now (2013) the interchange is pretty well-established.]

Some of the methods in this area remind me of stuff Christopher Alexander did in his 1964 book Notes on the Synthesis of Form, but it's been a long time since I read that, so my memory may be faulty.

— Stochastic block models are a particular class of probabilistic network models which have turned out to be extremely useful for community discovery; they get their own notebook.

See also: Ecology; Neuroscience; Signal Transduction, Gene Regulation and Control of Metabolism; Social Networks; Sociology of Science; Statistical Mechanics; Stochastic Block Models; Synchronization