Community detection in complex networks, 2015
Bidoni, Zeynab Bahrami
2010-2019
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.
text
application/pdf
2015-07-01
thesis
Master of Science (MS)
Clark Atlanta University
Computer and Information Sciences
Georgia--Atlanta
http://hdl.handle.net/20.500.12322/cau.td:2015_bidoni_zeynab_bahrami
