BIROn - Birkbeck Institutional Research Online

    Bayesian computational algorithms for social network analysis

    Caimo, A. and Gollini, Isabella (2016) Bayesian computational algorithms for social network analysis. In: Dehmer, M. and Shi, Y. and Emmert-Streib, F. (eds.) Computational Network Analysis with R: Applications in Biology, Medicine, and Chemistry. Wiley-VCH Verlag GmbH & Co. KGaA, pp. 63-82. ISBN 9783527339587.

    [img] Text
    1504.03152v1.pdf - Author's Accepted Manuscript
    Restricted to Repository staff only

    Download (579kB) | Request a copy


    Interest in statistical network analysis has grown massively in recent decades and its perspective and methods are now widely used in many scientific areas that involve the study of various types of networks for representing structure in many complex relational systems such as social relationships, information flows, and protein interactions. Social network analysis is based on the study of social relations between actors so as to understand the formation of social structures by the analysis of basic local relations. Statistical models have started to play an increasingly important role because they give the possibility to explain the complexity of social behaviour and to investigate issues on how the global features of an observed network may be related to local network structures. In this chapter, we review some of the most recent computational advances in the rapidly expanding field of statistical social network analysis using the R open-source software.


    Item Type: Book Section
    Keyword(s) / Subject(s): Bayesian analysis, Computational statistics, Social networks, Exponential random graph models, Latent variable models, R software
    School: Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Depositing User: Isabella Gollini
    Date Deposited: 04 Oct 2016 14:46
    Last Modified: 02 Aug 2023 17:26


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item