Doctoral Dissertations

Keywords and Phrases

Graph; Large-Scale; Mining; Motif; Network; Robustness

Abstract

"Networked systems are everywhere - such as the Internet, social networks, biological networks, transportation networks, power grid networks, etc. They can be very large yet enormously complex. They can contain a lot of information, either open and transparent or under the cover and coded. Such real-world systems can be modeled using graphs and be mined and analyzed through the lens of network analysis. Network analysis can be applied in recognition of frequent patterns among the connected components in a large graph, such as social networks, where visual analysis is almost impossible. Frequent patterns illuminate statistically important subgraphs that are usually small enough to analyze visually. Graph mining has different practical applications in fraud detection, outliers detection, chemical molecules, etc., based on the necessity of extracting and understanding the information yielded. Network analysis can also be used to quantitatively evaluate and improve the resilience of infrastructure networks such as the Internet or power grids. Infrastructure networks directly affect the quality of people's lives. However, a disastrous incident in these networks may lead to a cascading breakdown of the whole network and serious economic consequences. In essence, network analysis can help us gain actionable insights and make better data-driven decisions based on the networks. On that note, the objective of this dissertation is to improve upon existing tools for more accurate mining and analysis of real-world networks"--Abstract, page iv.

Advisor(s)

Leopold, Jennifer

Committee Member(s)

Lin, Dan
Jiang, Wei
Silvestri, Simone
Çetinkaya, Egemen K.

Department(s)

Computer Science

Degree Name

Ph. D. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2018

Journal article titles appearing in thesis/dissertation

  • FSMS: A frequent subgraph mining algorithm using mapping sets
  • All-inclusive frequent free subtree mining using automorphism list
  • Interactive visualization of robustness enhancement in scale-free networks with limited edge addition (RENEA)
  • Interactive visualization of robustness enhancement in scale-free networks against cascading failures
  • Motif-level robustness analysis of power grids

Pagination

xv, 129 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2018 Armita Abedijaberi, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11366

Electronic OCLC #

1051223386

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