Detecting Change in Longitudinal Social Networks

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 among 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. We nominate four types of dynamic network behaviors for investigation in this paper. These behaviors are not comprehensive; however, it is necessary to define a set of behaviors to focus our investigation of network change. The four behaviors we focus on are network stability, endogenous change, exogenous change, and initiated change. The first data set is the Newcomb fraternity data. The second set of data was collected on a group of mid-career U.S. Army officers in a week-long training exercise. The third data set contains the perceived connections among members of al Qaeda based on open sources, and the fourth data set is simulated using multiagent simulation. The results indicate that this methodology is able to detect change even with the high levels of uncertainty inherent in these data sets.