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A Statistical Approach To Characterize And Detect Degradation Within The Barabasi-Albert Network, Mohd-Fairul Mohd-Zaid
A Statistical Approach To Characterize And Detect Degradation Within The Barabasi-Albert Network, Mohd-Fairul Mohd-Zaid
Theses and Dissertations
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 …
A Risk Based Approach To Node Insertion Within Social Networks, Chancellor A. J. Johnstone
A Risk Based Approach To Node Insertion Within Social Networks, Chancellor A. J. Johnstone
Theses and Dissertations
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 …
Aggregation Techniques To Characterize Social Networks, Sara E. Sterling
Aggregation Techniques To Characterize Social Networks, Sara E. Sterling
Theses and Dissertations
Social network analysis focuses on modeling and understanding individuals of interest and their relationships. Aggregation of social networks can be used both to make analysis computationally easier on large networks, and to gain insight in subgroup interactions. Aggregation requires determining appropriate closely knit subgroups as well as choosing a measure or measures to represent the network data. This thesis provides the analyst with several techniques for using aggregation to analyze the characteristics of social networks. The contribution of this research lies in its ability to analyze a wide variety of social network structures and available data through two methods for …