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 of change, provide early warning of change, and enable faster response to change. By applying statistical process control techniques to social networks we can detect changes in these networks. Herein we describe this methodology and then illustrate it using three data sets. The first deals with the email communications among graduate students. The second is the perceived connections among members of al Qaeda based on open source data. The results indicate that this approach is able to detect change even with the high levels of uncertainty inherent in these data.

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