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Synthesizing Realistic Social Networks Using Personality Compatibility SYNTHESIZING REALISTIC SOCIAL NETWORKS USING PERSONALITY COMPATIBILITY by DANIEL ANTHONY O'NEIL A DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Modeling and Simulation Program to The School of Graduate Studies of The University of Alabama in Huntsville HUNTSVILLE, ALABAMA 2019 In presenting this dissertation in partial fulfillment of the requirements for a doctoral degree from The University of Alabama in Huntsville, I agree that the Library of this University shall make it freely available for inspection. I further agree that permission for extensive copying for scholarly purposes may be granted by my advisor or, in his/her absence, by the Chair of the Department or the Dean of the School of Graduate Studies. It is also understood that due recognition shall be given to me and to The University of Alabama in Huntsville in any scholarly use which may be made of any material in this dissertation. ___________________________ ___________ Daniel A. O’Neil (Date) ii iii DISSERTATION APPROVAL FORM Submitted by Daniel Anthony O'Neil in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Modeling and Simulation and accepted on behalf of the Faculty of the School of Graduate Studies by the dissertation committee. We, the undersigned members of the Graduate Faculty of The University of Alabama in Huntsville, certify that we have advised and/or supervised the candidate on the work described in this dissertation. We further certify that we have reviewed the dissertation manuscript and approve it in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Modeling and Simulation. iv v ABSTRACT The School of Graduate Studies The University of Alabama in Huntsville Degree: Doctor of Philosophy Program: Modeling and Simulation Name of Candidate: Daniel Anthony O'Neil Title: Synthesizing Realistic Social Networks Using Personality Compatibility_ Social structures and interpersonal relationships may be represented in abstract mathematical objects known as social networks. A social network consists of nodes corresponding to people and links between pairs of nodes corresponding to relationships between those people. Social networks can be constructed by examining groups of people and identifying the relationships of interest between them. There are circumstances where such empirical social networks are unavailable, or their use would be undesirable. Consequently, methods to generate synthetic social networks that are not identical to real- world networks but have desired structural similarities to them are valuable. A process for generating synthetic social networks based on attributing human personality types to the nodes and then stochastically adding links between nodes based on the compatibility of the nodes’ personalities was developed. Four algorithms for finding an effective assignment of personality types to nodes were developed and tested. Using the Myers-Briggs Type Indicator as a model of personality types, a compatibility table used by the algorithms was created. The four algorithms were evaluated for realism as measured by the similarity of the synthetic social networks to real-world exemplar networks. Based on 20 standard quantitative network metrics, synthesized social networks were compared to 14 real-world exemplar networks. Custom implementations of two randomized algorithm classes, Monte Carlo and Genetic, produced more realistic vi networks than the classic Erdős-Rényi algorithm. Two new heuristic algorithms, Probability Search and Compatibility-Degree Matching, produced more realistic networks than the well- known and widely-used Configuration Model algorithm. To confirm that the algorithms’ effectiveness was independent of a specific personality type model, 15 Iterated Prisoners’ Dilemma strategies were treated as personality types. The strategies were implemented, an Iterated Prisoners’ Dilemma round-robin tournament was conducted, and the tournament’s results were used as a personality compatibility table. The new Compatibility-Degree Matching algorithm again produced more realistic synthetic social networks than the Configuration Model algorithm. Finally, a new randomized algorithm to synthesize a sequence of revised social networks representing the evolution of a social network over time was developed. A Turing test showed that the synthesized social network sequences were indistinguishable from real-world exemplar sequences of evolving social networks. vii ACKNOWLEDGMENTS I thank God for the people who enabled the development of this dissertation. Especially, I am grateful to my adviser, Dr. Mikel Petty, for his guidance during the past nine years, for suggesting the topic, and for our creative collaboration on algorithms, code, and articles. Recommendations from other members of my dissertation committee regarding the scope of work and a validation approach were greatly appreciated. The Alabama Supercomputer Authority, which is funded by the State of Alabama, granted a copious amount of much needed processing time; that support is gratefully acknowledged. Deploying software on a supercomputer can be daunting; thankfully, Dr. David Young and his team patiently yet swiftly answered all my questions. This research was partially funded by the 2014 RADM Fred Lewis Postgraduate I/ITSEC Scholarship, awarded in association with the Interservice/Industry Training, Simulation and Education Conference and organized by the National Training and Simulation Association. The National Aeronautics and Space Administration funded several courses through the part time study program. My parents, Tony and Cozy O’Neil, provided additional funding. I thank these sponsors for significantly reducing the financial burden and associated emotional stress of this educational journey. A social network of family and friends provided emotional support. One of the reasons I cherish Marie O’Neil is she went back to college to accompany me on this journey. Brainstorming sessions with my supervisor and friend, Wes Brown, motivated me and clarified a reason for this research. My brother Chris, his wife Laura, and his sons Christopher, Kenneth, and Steven energized me as they listened to my stories about obstacles encountered during this journey. A compadre, Dan Shultz, cheered me up when I felt down. Finally, I thank my parents for their love and continuing encouragement. viii ix TABLE OF CONTENTS PAGE List of Tables ......................................................................................................................... xvi List of Figures ........................................................................................................................ xxi List of Abbreviations ........................................................................................................... xxiii Chapter 1 Introduction .............................................................................................................. 1 Chapter 2 Background Information .......................................................................................... 5 2.1 Graph theory .................................................................................................................... 5 2.2 Network theory and social network analysis ................................................................... 6 2.3 Classes of social networks ............................................................................................... 7 2.4 Data structures and attributes of social networks ............................................................ 9 2.5 Social network metrics .................................................................................................. 10 2.6 Personality models ........................................................................................................ 15 2.7 Iterated Prisoners’ Dilemma .......................................................................................... 18 Chapter 3 Motivation And Research Questions...................................................................... 22 Chapter 4 Literature Review ................................................................................................... 27 4.1 Social network metrics .................................................................................................. 27 4.2 Network generation methods ........................................................................................ 34 4.2.1 Random graph model .............................................................................................. 35 4.2.2 The Configuration Model ....................................................................................... 36 4.2.3 Exponential random graph model ........................................................................... 37 4.2.4 Stochastic block model ........................................................................................... 37 4.2.5 Small world model.................................................................................................. 39 x 4.2.6 Preferential attachment model ................................................................................ 39 4.2.7 Popularity similarity model .................................................................................... 39 4.2.8 Chung-Lu graph model ........................................................................................... 40 4.2.9 Degree correlation dK series .................................................................................
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