IDEAS home Printed from https://ideas.repec.org/a/cup/netsci/v3y2015i03p408-444_00.html
   My bibliography  Save this article

Clustering attributed graphs: Models, measures and methods

Author

Listed:
  • BOTHOREL, CECILE
  • CRUZ, JUAN DAVID
  • MAGNANI, MATTEO
  • MICENKOVÁ, BARBORA

Abstract

Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing, and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.

Suggested Citation

  • Bothorel, Cecile & Cruz, Juan David & Magnani, Matteo & Micenková, Barbora, 2015. "Clustering attributed graphs: Models, measures and methods," Network Science, Cambridge University Press, vol. 3(3), pages 408-444, September.
  • Handle: RePEc:cup:netsci:v:3:y:2015:i:03:p:408-444_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S2050124215000090/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Benati, Stefano & Ponce, Diego & Puerto, Justo & Rodríguez-Chía, Antonio M., 2022. "A branch-and-price procedure for clustering data that are graph connected," European Journal of Operational Research, Elsevier, vol. 297(3), pages 817-830.
    2. D’Ambra, Pasqua & Vassilevski, Panayot S. & Cutillo, Luisa, 2023. "Extending bootstrap AMG for clustering of attributed graphs," Applied Mathematics and Computation, Elsevier, vol. 447(C).
    3. Ignacio González García & Alfonso Mateos, 2021. "Use of Social Network Analysis for Tax Control in Spain," Hacienda Pública Española / Review of Public Economics, IEF, vol. 239(4), pages 159-197, November.
    4. Termeh Shafie & David Schoch, 2021. "Multiplexity analysis of networks using multigraph representations," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1425-1444, December.
    5. Liu, Wei & Chang, Zhenhai & Jia, Caiyan & Zheng, Yimei, 2022. "A generative node-attribute network model for detecting generalized structure and semantics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    6. Fengqin Tang & Chunning Wang & Jinxia Su & Yuanyuan Wang, 2020. "Spectral clustering-based community detection using graph distance and node attributes," Computational Statistics, Springer, vol. 35(1), pages 69-94, March.
    7. Benati, Stefano & Puerto, Justo & Rodríguez-Chía, Antonio M., 2017. "Clustering data that are graph connected," European Journal of Operational Research, Elsevier, vol. 261(1), pages 43-53.
    8. G. P. Clemente & A. Cornaro, 2023. "Community detection in attributed networks for global transfer market," Annals of Operations Research, Springer, vol. 325(1), pages 57-83, June.

    More about this item

    Statistics

    Access and download statistics

    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:cup:netsci:v:3:y:2015:i:03:p:408-444_00. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/nws .

    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.