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Detecting modules in dense weighted networks with the Potts method

by T. Heimo, J.M. Kumpula, K. Kaski, and J. Saramäki
Journal of Statistical Mechanics, P08007 (2008), also at arXiv: 0804.3457
Output type: publication
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We address the problem of multiresolution module detection in dense
weighted networks, where the modular structure is encoded in the weights rather
than topology. We discuss a weighted version of the q-state Potts method, which
was originally introduced by Reichardt and Bornholdt. This weighted method can be
directly applied to dense networks. We discuss the dependence of the resolution of
the method on its tuning parameter and network properties, using sparse and dense
weighted networks with built-in modules as example cases. Finally, we apply the
method to data on stock price correlations, and show that the resulting modules
correspond well to known structural properties of this correlation network.