Date of Award

9-15-2016

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Mathematics and Statistics

First Advisor

Christine Schubert Kabban, PhD.

Abstract

Social Network Analysis (SNA) is widely used by the intelligence community when analyzing the relationships between individuals within groups of interest. Hence, any tools that can be quantitatively shown to help improve the analyses are advantageous for the intelligence community. To date, there have been no methods developed to characterize a real world network as a Barabasi-Albert network which is a type of network with properties contained in many real-world networks. In this research, two newly developed statistical tests using the degree distribution and the L-moments of the degree distribution are proposed with application to classifying networks and detecting degradation within a network. The feasibility of these tests is shown by using the degree distribution for network and sub-network characterization of a selected scale-free real world networks. Further, sensitivity to the level of network degradation, via edge or node deletion, is examined with recommendation made as to the detectable size of degradation achievable by the statistical tests. Finally, the degree distribution of simulated Barabasi-Albert networks is investigated and results demonstrate that the theoretical distribution derived previously in the literature is not applicable to all network sizes. These results provide a foundation on which a statistically driven approach for network characterization can be built for network classification and monitoring.

AFIT Designator

AFIT-ENC-DS-16-S-003

DTIC Accession Number

AD1017866

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