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.
| Identifer | oai:union.ndltd.org:auctr.edu/oai:digitalcommons.auctr.edu:dissertations-3963 |
| Date | 01 July 2015 |
| Creators | Bidoni, Zeynab Bahrami |
| Publisher | DigitalCommons@Robert W. Woodruff Library, Atlanta University Center |
| Source Sets | Atlanta University Center |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | ETD Collection for AUC Robert W. Woodruff Library |
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