Spectral Analysis of Social Networks to Identify Periodicity |
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| Authors: | MATTHEW S. BOTHNER RICHARD HAYNES WONJAE LEE EDWARD BISHOP SMITH |
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| Affiliation: | 1. University of Chicago Booth School of Business , Chicago, Illinois, USA mbothner@chicagobooth.edu;3. Credit Suisse , New York, New York, USA;4. ISDPR , Seoul National University , Seoul, South Korea;5. Stephen M. Ross School of Business , University of Michigan , Ann Arbor, Michigan, USA |
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| Abstract: | Two key problems in the study of longitudinal networks are determining when to chunk continuous time 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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| Keywords: | leadership social networks status |
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