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    Spectral analysis of social networks to identify periodicity

    Access Status
    Fulltext not available
    Authors
    McCulloh, Ian
    Johnson, A.
    Carley, K.
    Date
    2012
    Type
    Journal Article
    
    Metadata
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    Citation
    McCulloh, I. and Johnson, A. and Carley, K. 2012. Spectral analysis of social networks to identify periodicity. Journal of Mathematical Sociology. 36 (2): pp. 80-96.
    Source Title
    Journal of Mathematical Sociology
    Additional URLs
    http://www.tandfonline.com/toc/gmas20/current
    ISSN
    15455874
    School
    School of Information Systems
    URI
    http://hdl.handle.net/20.500.11937/49333
    Collection
    • Curtin Research Publications
    Abstract

    Two key problems in the study of longitudinal networks are determining when to chunk continuoustime data into discrete time periods for network analysis and identifying periodicity in the data. In addition, statistical process control applied to longitudinal social network measures can be biased by the effects of relational dependence and periodicity in the data. Thus, the detection of change is often obscured by random noise. Fourier analysis is used to determine statistically significant periodic frequencies in longitudinal network data. Two approaches are then offered: using significant periods as a basis to chunk data for longitudinal network analysis or using the significant periods to filter the longitudinal data. E-mail communication collected at the United States Military Academy is examined.

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