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Clustering attributed graphs: Models, measures and methods

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  • 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
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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).
    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.

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