Date of Award


Document Type


Degree Name

Master of Science (MS)


Computer Science

First Advisor

Dr. Sanjukta Bhowmick

Second Advisor

Dr. Yuliya Lierler

Third Advisor

Dr. Robin A. Gandhi


Networks are popular models for representing interactions between entities in systems, such as in sociology, bioinformatics, and epidemiology. The entities in the networks are represented as vertices and their pair-wise interactions are represented as edges [1]. Many network metrics such as degree centrality (number of connections of an entity) and betweenness centrality (number of shortest paths passing through the entity) have been developed to rank the entities according to their importance [7] [10].Social networks are generally modeled on only one type of relation. Groups are open-ended, which means the number of participants and the time frame are not finite. Time frame may not cover significant events and their effect. How would the analysis change if we modeled the interactions and relationships of a closed group, over significant incidents? It is difficult to obtain real life data, because of the time commitment and privacy constraints. The next best option: Analyze fiction, which would give an indication of social relations [15].In this thesis, we study the effectiveness of these metrics in closed-form social interactions—particularly in the context of Shakespeare’s dramas [2] [18]. In plays the dialogues amongst characters are very precise to express the gist of their interactions in a short time frame. We are interested in understanding how this sort of interaction differs in a qualitative sense from the interactions seen in social media such as Facebook and Twitter. Our observations show that the popular network metrics are not always successful in correctly identifying the lead characters of the play and we propose a new method of creating two different types of networks from each play by using different criterion. Also the third type of network model, the Time Series Analysis considers the important characters of each play and filters edge lists based on them. Here the occurrence/influence of the important characters is examined from scene to scene and in turn from act to act from beginning to the end of each play we considered.


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 University of Nebraska at Omaha. Copyright 2014 Vikas Thotakuri.

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