Date of Award
Master of Science (MS)
Dr. Sanjukta Bhowmick
Dr. Hesham Ali
Dr. Kathryn Cooper
Real world large scale networks can be represented as graphs. This approach plays a key role in analysis in the domains of social networks  and bioinformatics , among others. Analyzing these networks is computationally complex and expensive, especially in terms of memory and time complexity. A popular technique subverting time and computation expense for analyzing networks is extracting substructures, which preserves more important information and less noise . In this work, we use special a special substructure called comparability, which preserves transitive orientation. Our motive is to extract a maximal comparability subgraph since no algorithm exists. Our algorithm is able to find a maximal comparability subgraph from both undirected and directed graphs. Finding a clique of given size is a NP-complete problem, so we must implement some additional constraints to maximize time efficiency. If the given input graph is chordal, then extraction of the clique of size n becomes a problem that is solvable in polynomial time. So we have written an algorithm to find the clique of given size, and implemented the algorithm to find a maximal chordal subgraph. Since we worked on two different special subgraphs, we compared our results to investigate whether the given graph is chordal or comparability in nature. In our research, we have proposed a parallel sampling method for efficient network analysis.
Balakrishnamoorthy, Muthunagai, "Detection of Comparability Subgraphs from Large Networks" (2017). Student Work. 2911.
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 Masters of Science University of Nebraska at Omaha. Copyright 2017 Muthunagai Balakrishnamoorthy.