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Identification of network modules by optimization of ratio association

(2007) CHAOS. 17(2).
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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 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. (c) 2007 American Institute of Physics.
Keywords
COMMUNITY STRUCTURE

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Citation

Please use this url to cite or link to this publication:

MLA
Angelini, L., et al. “Identification of Network Modules by Optimization of Ratio Association.” CHAOS, vol. 17, no. 2, 2007, doi:10.1063/1.2732162.
APA
Angelini, L., Boccaletti, S., Marinazzo, D., Pellicoro, M., & Stramaglia, S. (2007). Identification of network modules by optimization of ratio association. CHAOS, 17(2). https://doi.org/10.1063/1.2732162
Chicago author-date
Angelini, L., S. Boccaletti, Daniele Marinazzo, M. Pellicoro, and S. Stramaglia. 2007. “Identification of Network Modules by Optimization of Ratio Association.” CHAOS 17 (2). https://doi.org/10.1063/1.2732162.
Chicago author-date (all authors)
Angelini, L., S. Boccaletti, Daniele Marinazzo, M. Pellicoro, and S. Stramaglia. 2007. “Identification of Network Modules by Optimization of Ratio Association.” CHAOS 17 (2). doi:10.1063/1.2732162.
Vancouver
1.
Angelini L, Boccaletti S, Marinazzo D, Pellicoro M, Stramaglia S. Identification of network modules by optimization of ratio association. CHAOS. 2007;17(2).
IEEE
[1]
L. Angelini, S. Boccaletti, D. Marinazzo, M. Pellicoro, and S. Stramaglia, “Identification of network modules by optimization of ratio association,” CHAOS, vol. 17, no. 2, 2007.
@article{8697639,
  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. (c) 2007 American Institute of Physics.},
  articleno    = {023114},
  author       = {Angelini, L. and Boccaletti, S. and Marinazzo, Daniele and Pellicoro, M. and Stramaglia, S.},
  issn         = {1054-1500},
  journal      = {CHAOS},
  keywords     = {COMMUNITY STRUCTURE},
  language     = {eng},
  number       = {2},
  pages        = {6},
  title        = {Identification of network modules by optimization of ratio association},
  url          = {http://dx.doi.org/10.1063/1.2732162},
  volume       = {17},
  year         = {2007},
}

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