Estimating Personal Network Size with Non-random Mixing via Latent Kernels

Swupnil Sahai, Timothy Jones, Sarah K. Cowan, Tian Zheng

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    A major problem in the study of social networks is estimating the number of people an individual knows. However, there is no general method to account for barrier effects, a major source of bias in common estimation procedures. The literature describes approaches that model barrier effects, or non-random mixing, but they suffer from unstable estimates and fail to give results that agree with specialists’ knowledge. In this paper we introduce a model that builds off existing methods, imposes more structure, requires significantly fewer parameters, and yet allows for greater interpretability. We apply our model on responses gathered from a survey we designed and show that our conclusions better match what sociologists find in practice. We expect that this approach will provide more accurate estimates of personal network sizes and hence remove a significant hurtle in sociological research.

    Original languageEnglish (US)
    Title of host publicationComplex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018
    EditorsRenaud Lambiotte, Luis M. Rocha, Pietro Lió, Hocine Cherifi, Luca Maria Aiello, Chantal Cherifi
    PublisherSpringer Verlag
    Number of pages12
    ISBN (Print)9783030054106
    StatePublished - 2019
    Event7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018 - Cambridge, United Kingdom
    Duration: Dec 11 2018Dec 13 2018

    Publication series

    NameStudies in Computational Intelligence
    ISSN (Print)1860-949X


    Other7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018
    Country/TerritoryUnited Kingdom


    • Barrier effects
    • Kernel-based models
    • Non-random mixing
    • Personal network size estimation

    ASJC Scopus subject areas

    • Artificial Intelligence

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