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Noisy extremal optimization

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Abstract

Noisy extremal optimization is a new optimization-based heuristic designed to identify the community structure of complex networks by maximizing the modularity function. The extremal optimization algorithm evolves configurations that represent network covers, composed of nodes evaluated separately. Each iteration, a number of nodes having the worst fitness values are randomly assigned different communities. A network shifting procedure is used to induce a noise in the population as a diversity preserving mechanism. Numerical experiments, performed on synthetic and real-world networks, illustrate the potential of this approach.

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Notes

  1. Using the code available at https://sites.google.com/site/andrealancichinetti/software.

  2. http://www.orgnet.com, last accessed 9/3/2015.

  3. See footnote 1.

  4. By using the source code available at https://sites.google.com/site/andrealancichinetti/software.

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Authors and Affiliations

  1. Babes-Bolyai University, Cluj-Napoca, Romania

    Rodica Ioana Lung, Mihai Suciu & Noémi Gaskó

Authors
  1. Rodica Ioana Lung
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  2. Mihai Suciu
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  3. Noémi Gaskó
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Corresponding author

Correspondence to Rodica Ioana Lung.

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Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Additional information

Communicated by V. Loia.

Appendix

Appendix

The appendix contains supplementary numerical results and graphics with results.

See Figs. 8, 9, and 10 and Tables 1, 2, 3, 4, 5, 6 and 7.

Fig. 9
figure 9

NoisyEO: results obtained for different parameter settings; for each network, top figure represents the best NMI in the population; in the bottom the NMI of the individual having the best modularity; parameter settings: same as Fig.  8

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Fig. 10
figure 10

Evolution of NMI for 150 shifts, GN benchmark

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Lung, R.I., Suciu, M. & Gaskó, N. Noisy extremal optimization. Soft Comput 21, 1253–1270 (2017). https://doi.org/10.1007/s00500-015-1858-3

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  • DOI: https://doi.org/10.1007/s00500-015-1858-3

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