IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v35y2020i1d10.1007_s00180-019-00909-8.html
   My bibliography  Save this article

Spectral clustering-based community detection using graph distance and node attributes

Author

Listed:
  • Fengqin Tang

    (Huaibei Normal University)

  • Chunning Wang

    (Lanzhou University)

  • Jinxia Su

    (Lanzhou University)

  • Yuanyuan Wang

    (Lanzhou University)

Abstract

Community detection is one of the main research topics in network analysis. Most network data reveal a certain structural relationship between nodes and provide attributes describing them. Utilizing available node attributes can help uncover latent communities from an observed network. In this paper, we propose a method of uncovering latent communities using both network structural information and node attributes so that the nodes within each community not only connect to other nodes in similar patterns but also share homogeneous attributes. The proposed method transforms the graph distance of nodes to structural similarity via the Gaussian kernel function. The attribute similarity between nodes is also measured by the Gaussian kernel function. Our method takes advantage of spectral clustering by appending node attributes to the node representation obtained from the network structure. Further, the proposed method has the ability to automatically learn the degree to which different attributes contribute. The solid performance of the proposed method is demonstrated in simulated data and four real-world networks.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00909-8
    DOI: 10.1007/s00180-019-00909-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-019-00909-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-019-00909-8?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. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    2. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    3. 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.
    4. Mayiz B. Habbal & Haris N. Koutsopoulos & Steven R. Lerman, 1994. "A Decomposition Algorithm for the All-Pairs Shortest Path Problem on Massively Parallel Computer Architectures," Transportation Science, INFORMS, vol. 28(4), pages 292-308, November.
    5. M. E. J. Newman & Aaron Clauset, 2016. "Structure and inference in annotated networks," Nature Communications, Nature, vol. 7(1), pages 1-11, September.
    6. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    7. N. Binkiewicz & J. T. Vogelstein & K. Rohe, 2017. "Covariate-assisted spectral clustering," Biometrika, Biometrika Trust, vol. 104(2), pages 361-377.
    8. Raftery, Adrian E. & Dean, Nema, 2006. "Variable Selection for Model-Based Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 168-178, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Fengqin Tang & Xuejing Zhao & Cuixia Li, 2023. "Community Detection in Multilayer Networks Based on Matrix Factorization and Spectral Embedding Method," Mathematics, MDPI, vol. 11(7), pages 1-19, March.

    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. Babkin, Sergii & Stewart, Jonathan R. & Long, Xiaochen & Schweinberger, Michael, 2020. "Large-scale estimation of random graph models with local dependence," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    2. Lucy L. Gao & Daniela Witten & Jacob Bien, 2022. "Testing for association in multiview network data," Biometrics, The International Biometric Society, vol. 78(3), pages 1018-1030, September.
    3. Laleh Tafakori & Armin Pourkhanali & Riccardo Rastelli, 2022. "Measuring systemic risk and contagion in the European financial network," Empirical Economics, Springer, vol. 63(1), pages 345-389, July.
    4. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    5. Guang Ouyang & Dipak K. Dey & Panpan Zhang, 2020. "Clique-Based Method for Social Network Clustering," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 254-274, April.
    6. Samrachana Adhikari & Tracy Sweet & Brian Junker, 2021. "Analysis of longitudinal advice‐seeking networks following implementation of high stakes testing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1475-1500, October.
    7. Wentao Qu & Xianchao Xiu & Huangyue Chen & Lingchen Kong, 2023. "A Survey on High-Dimensional Subspace Clustering," Mathematics, MDPI, vol. 11(2), pages 1-39, January.
    8. West, Robert M. & House, Allan O. & Keen, Justin & Ward, Vicky L., 2015. "Using the structure of social networks to map inter-agency relationships in public health services," Social Science & Medicine, Elsevier, vol. 145(C), pages 107-114.
    9. Chiara Di Maria & Antonino Abbruzzo & Gianfranco Lovison, 2022. "Networks as mediating variables: a Bayesian latent space approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 1015-1035, October.
    10. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
    11. Ick Hoon Jin & Minjeong Jeon, 2019. "A Doubly Latent Space Joint Model for Local Item and Person Dependence in the Analysis of Item Response Data," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 236-260, March.
    12. Chih‐Sheng Hsieh & Hans van Kippersluis, 2018. "Smoking initiation: Peers and personality," Quantitative Economics, Econometric Society, vol. 9(2), pages 825-863, July.
    13. Salter-Townshend, Michael & Murphy, Thomas Brendan, 2013. "Variational Bayesian inference for the Latent Position Cluster Model for network data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 661-671.
    14. Chih‐Sheng Hsieh & Xu Lin, 2021. "Social interactions and social preferences in social networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(2), pages 165-189, March.
    15. Mai, Feng & Fry, Michael J. & Ohlmann, Jeffrey W., 2018. "Model-based capacitated clustering with posterior regularization," European Journal of Operational Research, Elsevier, vol. 271(2), pages 594-605.
    16. Irene Crimaldi & Michela Del Vicario & Greg Morrison & Walter Quattrociocchi & Massimo Riccaboni, 2015. "Homophily and Triadic Closure in Evolving Social Networks," Working Papers 3/2015, IMT School for Advanced Studies Lucca, revised May 2015.
    17. Sudhir Voleti & Praveen K. Kopalle & Pulak Ghosh, 2015. "An Interproduct Competition Model Incorporating Branding Hierarchy and Product Similarities Using Store-Level Data," Management Science, INFORMS, vol. 61(11), pages 2720-2738, November.
    18. Crespo Cuaresma, Jesus & Doppelhofer, Gernot, 2007. "Nonlinearities in cross-country growth regressions: A Bayesian Averaging of Thresholds (BAT) approach," Journal of Macroeconomics, Elsevier, vol. 29(3), pages 541-554, September.
    19. Teague R. Henry & Kathleen M. Gates & Mitchell J. Prinstein & Douglas Steinley, 2020. "Modeling Heterogeneous Peer Assortment Effects Using Finite Mixture Exponential Random Graph Models," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 8-34, March.
    20. Adrian E. Raftery, 2017. "Comment: Extending the Latent Position Model for Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1531-1534, October.

    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:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00909-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.