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Identification of network modules by optimization of ratio association

Leonardo Angelini·Stefano Boccaletti·Daniele Marinazzo·Mario Pellicoro·Sebastiano Stramaglia
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Abstract

We introduce a novel method for identifying the modular structures of a network based on the maximization of an objective function: the ratio association. This cost function arises when the communities detection problem is described in the probabilistic autoencoder frame. An analogy with kernel k-means methods allows to develop an efficient optimization algorithm, based on the deterministic annealing scheme. The performance of the proposed method is shown on a real data set and on simulated networks.

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Leonardo Angelini, Stefano Boccaletti, Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia. 2007-01-03. Identification of network modules by optimization of ratio association. https://doi.org/10.1063/1.2732162

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