Dynamic Network Change Detection

Abstract : Network data provides valuable insight into understanding complex organizations by modeling relational dependence between network agents. Detecting subtle changes in organizational behavior can alert analysts before the change significantly impacts the larger group. Statistical process control is applied to dynamic network measures of longitudinal data to quickly detect organizational change. The performance of 10 network measures and three algorithms are evaluated on simulated data. One of the algorithms and one of the network measures are used to demonstrate change detection on the Al-Qaeda terrorist network. There is no statistically significant difference in the performance of investigated algorithms, however, the cumulative sum control chart has a built-in estimate of the actual time a change may have occurred.

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