Betweenness centrality refers to the measure of a node’s influence on the transfer of items within a network. It is a mechanism used to identify participants within an interconnected system that are responsible for processing high frequencies of traffic. This thesis examines the performance characteristics of a specialized artificial intelligence algorithm known as Ant Colony Optimization and its application in the field of social network analysis. The modeling and examination of such algorithms is important largely because of its ability to span across multiple fields of study as well as a variety of network applications. The effects of network analysis can be felt everywhere. Business and military intelligence; hardware resiliency (fault tolerance); network routing, are but a few of the fields that can and do benefit from research due in part to specialized network analysis. In this research paper, extensive social networks are built, execution time is measured, and algorithm viability is tested through the identification of high frequency nodes within real social networks.
|Advisor:||Podnar, Hrvoje, Lancor, Lisa|
|School:||Southern Connecticut State University|
|School Location:||United States -- Connecticut|
|Source:||MAI 55/04M(E), Masters Abstracts International|
|Subjects:||Social research, Artificial intelligence, Computer science|
|Keywords:||Ant colony optimization, Betweenness centrality, Social network analysis|
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