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Multiplicative latent factor models for description and prediction of social networks

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  • Peter D. Hoff

    (University of Washington)

Abstract

We discuss a statistical model of social network data derived from matrix representations and symmetry considerations. The model can include known predictor information in the form of a regression term, and can represent additional structure via sender-specific and receiver-specific latent factors. This approach allows for the graphical description of a social network via the latent factors of the nodes, and provides a framework for the prediction of missing links in network data.

Suggested Citation

  • Peter D. Hoff, 2009. "Multiplicative latent factor models for description and prediction of social networks," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 261-272, December.
    • Handle: RePEc:spr:comaot:v:15:y:2009:i:4:d:10.1007_s10588-008-9040-4
    DOI: 10.1007/s10588-008-9040-4
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    References listed on IDEAS

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    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. Aldous, David J., 1981. "Representations for partially exchangeable arrays of random variables," Journal of Multivariate Analysis, Elsevier, vol. 11(4), pages 581-598, December.
    3. Peter D. Hoff, 2005. "Bilinear Mixed-Effects Models for Dyadic Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 286-295, March.
    4. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
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    Cited by:

    1. Tracy Sweet & Samrachana Adhikari, 2020. "A Latent Space Network Model for Social Influence," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 251-274, June.
    2. Chungmok Lee & Minh Pham & Myong K. Jeong & Dohyun Kim & Dennis K. J. Lin & Wanpracha Art Chavalitwongse, 2015. "A Network Structural Approach to the Link Prediction Problem," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 249-267, May.
    3. Yu Zhang & Yu Wu, 2012. "How behaviors spread in dynamic social networks," Computational and Mathematical Organization Theory, Springer, vol. 18(4), pages 419-444, December.
    4. Terrence D. Jorgensen & Aditi M. Bhangale & Yves Rosseel, 2024. "Two-Stage Limited-Information Estimation for Structural Equation Models of Round-Robin Variables," Stats, MDPI, vol. 7(1), pages 1-34, February.
    5. Fithian, William & Josse, Julie, 2017. "Multiple correspondence analysis and the multilogit bilinear model," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 87-102.
    6. Paul, Sudeshna & Keating, Nancy L. & Landon, Bruce E. & O'Malley, A. James, 2014. "Results from using a new dyadic-dependence model to analyze sociocentric physician networks," Social Science & Medicine, Elsevier, vol. 117(C), pages 67-75.
    7. Sosa, Juan & Betancourt, Brenda, 2022. "A latent space model for multilayer network data," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    8. Anish Agarwal & Keegan Harris & Justin Whitehouse & Zhiwei Steven Wu, 2023. "Adaptive Principal Component Regression with Applications to Panel Data," Papers 2307.01357, arXiv.org, revised Oct 2023.
    9. Bräuning, Falk & Koopman, Siem Jan, 2020. "The dynamic factor network model with an application to international trade," Journal of Econometrics, Elsevier, vol. 216(2), pages 494-515.
    10. François Caron & Emily B. Fox, 2017. "Sparse graphs using exchangeable random measures," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1295-1366, November.
    11. Paul, Sudeshna & Keating, Nancy L. & Landon, Bruce E. & O’Malley, A. James, 2015. "Reprint of: Results from using a new dyadic-dependence model to analyze sociocentric physician networks," Social Science & Medicine, Elsevier, vol. 125(C), pages 51-59.

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