Date of Award

7-1-2015

Degree Type

Thesis

University or Center

Clark Atlanta University(CAU)

Degree Name

M.S.

Department

Computer and Information Sciences

First Advisor

Roy George, Ph.D.

Abstract

This research study has produced advances in the understanding of communities within a complex network. A community in this context is defined as a subgraph with a higher internal density and a lower crossing density with respect to other subgraphs. In this study, a novel and efficient distance-based ranking algorithm called the Correlation Density Rank (CDR) has been proposed and is utilized for a broad range of applications, such as deriving the community structure and the evolution graph of the organizational structure from a dynamic social network, extracting common members between overlapped communities, performance-based comparison between different service providers in a wireless network, and finding optimal reliability-oriented assignment tasks to processors in heterogeneous distributed computing systems. The experiments, conducted on both synthetic and real datasets, demonstrate the feasibility and applicability of the framework.

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Notice to Users, Transmittal, and Statement of Understanding

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