Date of Award
3-26-2015
Document Type
Thesis
Degree Name
Master of Science in Operations Research
Department
Department of Operational Sciences
First Advisor
Jennifer L. Geffre, PhD.
Abstract
Social Network Analysis (SNA) is a primary tool for counter-terrorism operations, ranging from resiliency and influence to interdiction on threats stemming from illicit overt and clandestine network operations. In an ideal world, SNA would provide a perfect course of action to eliminate dangerous situations that terrorist organizations bring. Unfortunately, the covert nature of terrorist networks makes the effects of these techniques unknown and possibly detrimental. To avoid potentially harmful changes to enemy networks, tactical involvement must evolve, beginning with the intelligent use of network in filtration through the application of the node insertion problem. The framework for the node insertion problem includes a risk-benefit model to assess the utility of various node insertion scenarios. This model incorporates local, intermediate and global SNA measures, such as Laplacian centrality and assortative mixing, to account for the benefit and risk. Application of the model to the Zachary Karate Club produces a set of recommended insertion scenarios. A designed experiment validates the robustness of the methodology against network structure and characteristics. Ultimately, the research provides an SNA method to identify optimal and near-optimal node insertion strategies and extend past node utility models into a general form with the inclusion of benefit, risk, and bias functions.
AFIT Designator
AFIT-ENS-MS-15-M-136
DTIC Accession Number
ADA622913
Recommended Citation
Johnstone, Chancellor A. J., "A Risk Based Approach to Node Insertion within Social Networks" (2015). Theses and Dissertations. 118.
https://scholar.afit.edu/etd/118