<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Changes in observed social networks may signal an underlying change within an organization, and may even predict significant events or behaviors. The breakdown of a team’s effectiveness, the emergence of informal leaders, or the preparation of an attack by a clandestine network may all be associated with changes in the patterns of interactions between group members. The ability to systematically, statistically, effectively and efficiently detect these changes has the potential to enable the anticipation, early warning, and faster response to both positive and negative organizational activities. By applying statistical process control techniques to social networks we can rapidly detect changes in these networks. Herein we describe this methodology and then illustrate it using four data sets, of which the first is the Newcomb fraternity data, the second set of data is collected on a group of mid-career U.S. Army officers in a week long training exercise, the third is the perceived connections among members of al Qaeda based on open source, and the fourth data set is simulated using multi-agent simulation. The results indicate that this approach is able to detect change even with the high levels of uncertainty inherent in these data.</p></abstract>

Changes in observed social networks may signal an underlying change within an organization, and may even predict significant events or behaviors. The breakdown of a team’s effectiveness, the emergence of informal leaders, or the preparation of an attack by a clandestine network may all be associated with changes in the patterns of interactions between group members. The ability to systematically, statistically, effectively and efficiently detect these changes has the potential to enable the anticipation, early warning, and faster response to both positive and negative organizational activities. By applying statistical process control techniques to social networks we can rapidly detect changes in these networks. Herein we describe this methodology and then illustrate it using four data sets, of which the first is the Newcomb fraternity data, the second set of data is collected on a group of mid-career U.S. Army officers in a week long training exercise, the third is the perceived connections among members of al Qaeda based on open source, and the fourth data set is simulated using multi-agent simulation. The results indicate that this approach is able to detect change even with the high levels of uncertainty inherent in these data.

Detecting Change in Longitudinal Social Networks

Journal of Social Structure

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Detecting Change in Longitudinal Social Networks"}

Changes in observed social networks may signal an underlying change within an organization, and may even predict significant events or behaviors. The breakdown...

Detecting Change in Longitudinal Social Networks

Online veröffentlicht: 13. Jan. 2020

Seitenbereich: 1 - 37

DOI: https://doi.org/10.21307/joss-2019-031

Schlüsselwörter
Statistical models for social networks, longitudinal social network analysis, Statistical Process Control, CUSUM, change detection

© 2019 Ian McCulloh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Detecting Change in Longitudinal Social Networks

Online veröffentlicht: 13. Jan. 2020

Seitenbereich: 1 - 37

DOI: https://doi.org/10.21307/joss-2019-031

SchlüsselwörterStatistical models for social networks, longitudinal social network analysis, Statistical Process Control, CUSUM, change detection

© 2019 Ian McCulloh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Statistical models for social networks, longitudinal social network analysis, Statistical Process Control, CUSUM, change detection