Date of Award
University or Center
Clark Atlanta University(CAU)
Computer and Information Sciences
Roy George, Ph.D.
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.
Bidoni, Zeynab Bahrami, "Community detection in complex networks" (2015). ETD Collection for AUC Robert W. Woodruff Library. 2447.