IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v595y2022ics0378437122001170.html
   My bibliography  Save this article

An algorithm for network community structure determination by surprise

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122001170
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.127063?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Rodrigo Aldecoa & Ignacio Marín, 2011. "Deciphering Network Community Structure by Surprise," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-8, September.
    3. 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).
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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).
    3. Radillo-Ochoa, Diego & Rodríguez-Hernández, Andrea & Terrero-Escalante, César A., 2023. "Bifurcation in cellular evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    4. Vincent Labatut & Jean-Michel Balasque, 2012. "Detection and Interpretation of Communities in Complex Networks: Methods and Practical Application," Post-Print hal-00633653, HAL.
    5. 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).
    6. Wang, Zhixiao & Rui, Xiaobin & Yuan, Guan & Cui, Jingjing & Hadzibeganovic, Tarik, 2021. "Endemic information-contagion outbreaks in complex networks with potential spreaders based recurrent-state transmission dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:595:y:2022:i:c:s0378437122001170. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.