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We demonstrate that usual measures of modularity that only take into account the static, topological structure of networks, cannot identify the community structure disclosed by population dynamics.
Not surprisingly, multiple different measures of modularity have been developed [ 8, 49- 54].
Problems like these can be avoided by using functional measures of modularity.
This measure can be analyzed much in the same way as other measures of modularity, but is applied a posteriori.
Further, our results call for exploring new measures of modularity and network communities that better correspond to functional categorizations.
Established measures of modularity measure basically the same, they only correct for the expectation of within-module links in non-modular random networks in a different way.
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Furthermore, any measure of modularity could be calculated a posteriori or during modularity optimization.
The cluster coefficient (c) is a measure of modularity of a graph.
In our study, Newman's metric [ 5] is used as a measure of modularity.
We used a standard measure of modularity [ 18, 41], to evaluate the modularity of each metabolic network.
The measure of modularity we used here was based on the reactions contained in fully coupled sets (FCSs) [ 28].
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measures of misery
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