Date of Award

12-2011

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Sanjukta Bhowmick

Second Advisor

Dr. Hesham Ali

Third Advisor

Dr. Harvey Siy

Fourth Advisor

Dr. Dhundy Bastola

Abstract

Diverse application areas, such as social network, epidemiology, and software engineering consist of systems of objects and their relationships. Such systems are generally modeled as graphs. Graphs consist of vertices that represent the objects, and edges that represent the relationships between them. These systems are data intensive and it is important to correctly analyze the data to obtain meaningful information. Combinatorial metrics can provide useful insights for analyzing these systems. In this thesis, we use the graph based metrics such as betweenness centrality, clustering coefficient, articulation points, etc. for analyzing instances of large change in evolving networks (Software Engineering), and identifying points of similarity (Gene Expression Data). Computations of combinatorial properties are expensive and most real world networks are not static. As the network evolves these properties have to be recomputed. In the last part of thesis, we develop a fast algorithm that avoids redundant recomputations of communities in dynamic networks.

Comments

A Thesis Presented to the Department of Computer Science and the Faculty of the Graduate College University of Nebraska In Partial Fulfillment of the Requirements for the Degree Master of Science. Copyright 2011 Prashant Shivaji Paymal.

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