Nonlinear Sciences – Adaptation and Self-Organizing Systems
Scientific paper
2010-05-19
Nonlinear Sciences
Adaptation and Self-Organizing Systems
8 pages, presented at the 2009 European Conference on Artificial Life
Scientific paper
When is it safe to approximate a complicated random Boolean network (RBN) as a simplified, easier to model RBN? When can static measures of network structure be reliably used to infer the network's dynamics? This simple experiment tests the ability of disjoint modular RBNs to approximate the dynamics of progressively more interconnected RBNs, while characterizing the performance of both static and dynamic measures of modularity as both break down. We find that, at least in the small networks investigated, the Newman 2004 [1] measure of static modularity performs as well as a more complex dynamic measure of modularity, and that the progressively increasing failure of one tracks that of the other. The dynamic measure is based on the Hamming distance of attractor schemata in rewired networks from those in perfectly modular networks. This result holds for a range of p-values.
Deaton Shaun
Frey Seth
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