Social network change detection

Social network change detection (SNCD) is a process of monitoring social networks to determine when significant changes to their organizational structure occur and what caused them. This scientific approach combines analytical techniques from social network analysis with those from statistical process control. SNCD can be used to detect when significant changes occur in a network. In application, it requires the use of statistical process control charts to detect changes in observable network measures. By taking measures of a network over time, a control chart can be used to signal when significant changes occur in the network.

This approach has been demonstrated to be effective on several real world data sets. A social network was created for a group of 24 Army officers going through a 1 year graduate program at Columbia University (McCulloh, et al., 2007). SNCD was able to detect the group's comprehensive exam and identified the most likely time of change to be the week that study questions were sent to the group. An open source social network of the Al-Qaeda terrorist organization was monitored using SNCD, and it signaled a change in the organization prior to the September 11 terrorist attacks on the Pentagon and the World Trade Center (McCulloh, et al., 2007). SNCD has also been demonstrated to be effective on simulated data.

SNCD was initially proposed by Major Ian McCulloh, an assistant professor in the U.S. Military Academy's Network Science Center in 2006. Since then, SNCD has been presented at a variety of venues from NetSci2007 in New York City to the International Network for Social Network Analysis annual conference in 2008, to the Military Operations Research Society Working Group on emerging threats and social networks in 2008.

ee also

* Complex networks
* Exponential random graph model
* Network Probability Matrix
* Network science
* Social network analysis

References

* Baller, D., McCulloh, I., Carley, K.M., and Johnson, A.N. (2008). Specific Communication Network Measure Distribution Estimation. Sunbelt XXVIII, the annual conference for the International Network of Social Network Analysts, Saint Petersburg, FL, 24 January, 2008.
* Carley, K. M. (2003). Dynamic network analysis. In P. Pattison (Ed.), Dynamic social network analysis: Workshop summary and papers: 133-145. Washington D.C.: The National Academies Press.
* McCulloh, I., and Carley, K.M. (2008). Social Network Change Detection. Carnegie Mellon University Technical Report, CMU-CS-08-116.
* McCulloh, I., Garcia, G., Tardieu, K., MacGibon, J., Dye, H., Moores, K., Graham, J. M., & Horn, D. B. (2007). IkeNet: Social network analysis of e-mail traffic in the Eisenhower Leadership Development Program. (Technical Report, No. 1218). Arlington, VA: U.S. Army Research Institute for the Behavioral and Social Sciences.
* McCulloh, I., Lospinoso, J., and Carley, K.M. (2007). Social Network Probability Mechanics. Proceedings of the World Scientific Engineering Academy and Society 12th International Conference on Applied Mathematics, Cairo, Egypt, 29-31 December, 2007, pp. 319-325,
* McCulloh, I., Webb, M., Carley, K.M. (2007). Social Network Monitoring of Al-Qaeda. Network Science Report, Vol 1, pp 25-30.