Detecting modules in dense weighted networks with the Potts method

Abstract

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 stock price correlation data, and show that the resulting modules correspond well to known structural properties of this correlation network.

Analyzing and modeling real-world phenomena with complex networks: a survey of applications

“…Pan and Sinha [445] investigated the stock price fluctuations in the National Stock Exchange (NSE) of India, and found that: (i) stocks in emerging markets are more correlated than in developed ones and (ii) the Indian market evolved into clusters corresponding to business sectors. The clustering property has also been investigated in the NYSE [446][447][448][449]. Bolgorian and Raei [450] considered thresholding in correlations between daily stock prices to build a network.…”

A Game Theoretic Framework for Community Detection

“…The game theoretic approach requires attention on two important issues - (1) what are the essential properties/requirements that a payoff function should satisfy and (2) what are the important game theory concepts that can be brought into applications to achieve a "good" community structure.…”

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“…In fact, http://www.casmodeling.com/content/1/1/5 the idea of viewing datasets as networks of data has already been developed in previous works. Just to cite few, Heimo et al 2008 studied the problem of multiresolution module detection in dense weighted networks, using a weighted version of the q-state Potts method. Mucha et al 2010 developed a generalized framework to study community structures of arbitrary multislice networks.…”