Detecting Change in Human Social Behavior Simulation

Abstract

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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Document Details

Document Type
Technical Report
Publication Date
Aug 19, 2008
Accession Number
ADA488527

Entities

People

  • Ian Mcculloh
  • Kathleen Carley

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Army
  • Change Detection
  • Command And Control
  • Communication Networks
  • Data Science
  • Detection
  • Human Behavior
  • Information Science
  • Military Organizations
  • Military Research
  • Network Science
  • Reconnaissance
  • Simulations
  • Social Networks
  • Social Sciences
  • Statistical Processes
  • Statistics

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computer Networking
  • Statistical inference.