Discovering Functional Communities in Dynamical Networks

Details Discovering Functional Communities in Dynamical Networks Discovering Functional Communities in Dynamical Networks

2006-09-07

arxiv.org/abs/q-bio/0609008v2

Biology

Quantitative Biology

Neurons and Cognition

18 pages, 4 figures, Springer "Lecture Notes in Computer Science" style. Forthcoming in the proceedings of the workshop "Stati

Scientific paper

Many networks are important because they are substrates for dynamical systems, and their pattern of functional connectivity can itself be dynamic -- they can functionally reorganize, even if their underlying anatomical structure remains fixed. However, the recent rapid progress in discovering the community structure of networks has overwhelmingly focused on that constant anatomical connectivity. In this paper, we lay out the problem of discovering_functional communities_, and describe an approach to doing so. This method combines recent work on measuring information sharing across stochastic networks with an existing and successful community-discovery algorithm for weighted networks. We illustrate it with an application to a large biophysical model of the transition from beta to gamma rhythms in the hippocampus.

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