Date on Master's Thesis/Doctoral Dissertation

5-2018

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Computer Engineering and Computer Science

Degree Program

Computer Science and Engineering, PhD

Committee Chair

Elmaghraby, Adel

Committee Co-Chair (if applicable)

Alexander, Suraj

Committee Member

Alexander, Suraj

Committee Member

Altiparmak, Nihat

Committee Member

Chang, Dar-Jen

Committee Member

Imam, Ibrahim

Author's Keywords

network science; mobile networks; social networks; evolutionary clustering; evolutionary centrality

Abstract

Network Science is one of the important and emerging fields in computer science and engineering that focuses on the study and analysis of different types of networks. The goal of this dissertation is to design and develop network science algorithms that can be used to study and analyze mobile networks. This can provide essential information and knowledge that can help mobile networks service providers to enhance the quality of the mobile services. We focus in this dissertation on the design and analysis of different network science techniques that can be used to analyze the dynamics of mobile networks. These techniques include evolutionary clustering, classification, discovery of maximal cliques, and evolutionary centrality algorithms. We proposed evolutionary clustering and evolutionary centrality algorithms that can be used to dynamically discover clusters and central nodes in mobile networks. Overall, the experimental results show that the proposed evolutionary algorithms are robust to short-term variations but reflects long-term trends and can be used effectively to analyze the dynamics of mobile networks.

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