Abstract
This paper describes the challenges posed by pattern-of-life variations when carrying out automated detection of abnormal events (change detection) in longitudinal (over-time) social network data sets using standard social network measures. In this paper we present a new scheme for substantially removing pattern-of-life variations from longitudinal social network measures. This new approach is based on a model in which pattern-of-life variations are modeled as time-dependent periodic multiplicative weights on the likelihood of initiating a new post in a social network. Unfortunately, analysis of real-world social network data reveals that the time-dependent weights change over time as well. Therefore, an approach for adaptively determining the time-dependent periodic multiplicative weights has been developed. A complete methodology for Adaptive Multiplicative Compensation for Pattern-of-Life variations is described and the methodology is tested on a suitable social media data set. The impact of pattern-of-life variations on the test over-time data set is reduced by up to a factor of 4X by the algorithm presented. The impact on the occurrences of false positive events (labeling a time point as a “change” when it is not) and the impact on the occurrences of false negative events (labeling a time point as “normal” when it really represented a change) clear in the test data set.
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Acknowledgements
This work was supported in part by the Office of Naval Research (ONR) Awards N00014-21-1-2765 & N00014-21-1-2229. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ONR or U.S. government.
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Carley, L.R., Carley, K.M. (2024). Improved Change Detection in Longitudinal Social Network Measures Subject to Pattern-of-Life Variations. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-031-53503-1_27
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