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Deciphering Network Community Structure by Surprise

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  • Rodrigo Aldecoa
  • Ignacio Marín

Abstract

The analysis of complex networks permeates all sciences, from biology to sociology. A fundamental, unsolved problem is how to characterize the community structure of a network. Here, using both standard and novel benchmarks, we show that maximization of a simple global parameter, which we call Surprise (S), leads to a very efficient characterization of the community structure of complex synthetic networks. Particularly, S qualitatively outperforms the most commonly used criterion to define communities, Newman and Girvan's modularity (Q). Applying S maximization to real networks often provides natural, well-supported partitions, but also sometimes counterintuitive solutions that expose the limitations of our previous knowledge. These results indicate that it is possible to define an effective global criterion for community structure and open new routes for the understanding of complex networks.

Suggested Citation

  • Rodrigo Aldecoa & Ignacio Marín, 2011. "Deciphering Network Community Structure by Surprise," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-8, September.
  • Handle: RePEc:plo:pone00:0024195
    DOI: 10.1371/journal.pone.0024195
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    References listed on IDEAS

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    1. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
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    Cited by:

    1. Henry Dorrian & Jon Borresen & Martyn Amos, 2013. "Community Structure and Multi-Modal Oscillations in Complex Networks," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
    2. Fang, Wenyi & Wang, Xin & Liu, Longzhao & Wu, Zhaole & Tang, Shaoting & Zheng, Zhiming, 2022. "Community detection through vector-label propagation algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    3. Zhan, Weihua & Deng, Lei & Guan, Jihong & Niu, Jun & Sun, Dechao, 2020. "Revealing dynamic communities in networks using genetic algorithm with merge and split operators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    4. Gamermann, Daniel & Pellizzaro, José Antônio, 2022. "An algorithm for network community structure determination by surprise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).

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