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An algorithm for network community structure determination by surprise

Author

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  • Gamermann, Daniel
  • Pellizzaro, José Antônio

Abstract

A community in a network is an intuitive idea for which there is no consensus on its objective mathematical definition. Therefore, different algorithms and metrics have been suggested in order to identify these structures in graphs. In this work, we propose a new benchmark and a new approach based on a metric known as surprise. We compare our approach to several others in the literature, in different kinds of benchmarks, including our own (that tackles separately the different ways in which one may degrade a network’s community structure) and discuss the different biases we identify for each algorithm and benchmark. In particular, we identify a possible flaw in the way the LFR benchmark constructs its communities and that algorithms suffering from bad resolution are biased towards identifying communities with similar sizes. We show that the surprise based approaches perform better than the modularity based ones, specially for heterogeneous graphs (with very different community sizes coexisting).

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:595:y:2022:i:c:s0378437122001170
    DOI: 10.1016/j.physa.2022.127063
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    References listed on IDEAS

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    1. Gamermann, D. & Triana-Dopico, J. & Jaime, R., 2019. "A comprehensive statistical study of metabolic and protein–protein interaction network properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    2. Daniel Gamermann & Arnau Montagud & J Alberto Conejero & Pedro Fernández de Córdoba & Javier F Urchueguía, 2019. "Large scale evaluation of differences between network-based and pairwise sequence-alignment-based methods of dendrogram reconstruction," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-13, September.
    3. Rodrigo Aldecoa & Ignacio Marín, 2010. "Jerarca: Efficient Analysis of Complex Networks Using Hierarchical Clustering," PLOS ONE, Public Library of Science, vol. 5(7), pages 1-7, July.
    4. Rodrigo Aldecoa & Ignacio Marín, 2011. "Deciphering Network Community Structure by Surprise," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-8, September.
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