title: Identification of network modules by optimization of ratio association.
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 us to develop an efficient optimization algorithm, based on the deterministic annealing scheme. The performance of the proposed method is shown on real data sets and on simulated networks.
authors.affiliation: TIRES-Center of Innovative Technologies for Signal Detection and Processing, Dipartimento Interateneo di Fisica, University of Bari, 70126 Bari, Italy.
authors.forename: L
authors.initials: L
authors.lastname: Angelini
authors.forename: S
authors.initials: S
authors.lastname: Boccaletti
authors.forename: D
authors.initials: D
authors.lastname: Marinazzo
authors.forename: M
authors.initials: M
authors.lastname: Pellicoro
authors.forename: S
authors.initials: S
authors.lastname: Stramaglia