Spectral analysis of social networks to identify periodicity
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...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Taylor & Francis
2012
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| Online Access: | http://www.tandfonline.com/toc/gmas20/current http://hdl.handle.net/20.500.11937/49333 |
| _version_ | 1848758218338074624 |
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| author | McCulloh, Ian Johnson, A. Carley, K. |
| author_facet | McCulloh, Ian Johnson, A. Carley, K. |
| author_sort | McCulloh, Ian |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:40:30Z |
| format | Journal Article |
| id | curtin-20.500.11937-49333 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:40:30Z |
| publishDate | 2012 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-493332017-03-15T22:56:12Z Spectral analysis of social networks to identify periodicity McCulloh, Ian Johnson, A. Carley, K. statistical process control longitudinal networks Fourier analysis network dynamics social network analysis 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. 2012 Journal Article http://hdl.handle.net/20.500.11937/49333 http://www.tandfonline.com/toc/gmas20/current Taylor & Francis restricted |
| spellingShingle | statistical process control longitudinal networks Fourier analysis network dynamics social network analysis McCulloh, Ian Johnson, A. Carley, K. Spectral analysis of social networks to identify periodicity |
| title | Spectral analysis of social networks to identify periodicity |
| title_full | Spectral analysis of social networks to identify periodicity |
| title_fullStr | Spectral analysis of social networks to identify periodicity |
| title_full_unstemmed | Spectral analysis of social networks to identify periodicity |
| title_short | Spectral analysis of social networks to identify periodicity |
| title_sort | spectral analysis of social networks to identify periodicity |
| topic | statistical process control longitudinal networks Fourier analysis network dynamics social network analysis |
| url | http://www.tandfonline.com/toc/gmas20/current http://hdl.handle.net/20.500.11937/49333 |