Detecting Change in Human Social Behavior Simulation
CARNEGIE-MELLON UNIV PITTSBURGH PA INST OF SOFTWARE RESEARCH INTERNAT
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The performance of social network change detection SNCD is evaluated using a multi-agent simulation of company level U.S Army Infantry organizations. Agent interaction is probabilistic, with increased likelihood of communication based on similarity in skills, role, sub-unit of assignment, military rank, and general personality homophily. Various social network measures are monitored for change over time with a Cumulative Sum CUSUM control chart, an Exponentially Weighted Moving Average EWMA, a scan statistic, and a Hamming Distance. Findings show that the average betweenness, the average closeness, and the standard deviation of eigenvector centrality are social network measures that are well-suited for SNCD. This research further supports the efficacy of SNCD using statistical process control charts.
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