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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Shalizi, C. R., Camperi, M. F., & Klinkner, K. L. (2007). Discovering functional communities in dynamical networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4503 LNCS, pp. 140–157). Springer Verlag. https://doi.org/10.1007/978-3-540-73133-7_11
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