Exploring individual status and collaboration: three studies using social network analysis methods
Links to Fileshttp://library.towson.edu/cdm/ref/collection/etd/id/47392
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Type of Workapplication/pdf
xv, 162 pages
DepartmentTowson University. Department of Computer and Information Sciences
Among the most trendy and ubiquitous services provided on the web are online social networking sites such as Twitter, Facebook, and LinkedIn to name a few. Participants of these social networking sites form and engage in a complex network system where individuals share information about their daily activities; keep abreast with families, friends, and acquaintances; as well as share and distribute knowledge and informational content to both their ‘friends’ in the network. The dynamic nature of online social networks and the dramatic increase in their popularity since the last decade provide a large-scale data store for the study of the structure, patterns of behavior, relationship roles and other resultant characteristics of the network graph among the social entities within the social network. This dissertation that examines various aspects of Social Network Analysis (SNA) contains one static SNA essay and two dynamic SNA cases studies. In the first essay, the focus is on the structure of online social networks, specifically Twitter, and how sitting United States governors utilize the social network to distribute information to citizens. Many of the key conceptual theories of static SNA including structural balance, transitivity, reciprocity, social cohesion, influence, dominance, conformity and social role are underscored. The study provides evidence that although transitivity and reciprocity occur with a high level of interaction, there is very little dominance in the structure of the network. The result supports other studies in this area. In addition, the study provides further evidence to support structural balance (or imbalance) of geographical homophily since the majority of the friends and followers of these governors are from their state. Moreover, the results indicate a network imbalance resulting in the isolation of the dissemination of resources and services to citizens. In the first case study, SNA graph theory is used to analyze the evolution and collaborations of cybersecurity education researchers in the domain of academic research. The primary emphasis is on analytic principles and concepts and the use of graph theory to represent the network data. Further, using the graph theories, we utilized SNA analytic tools to help make predictions about the principal network structure. This case study discusses many of the primary concepts of interpreting patterns of social ties among individuals in the network community. We examined the patterns of interactions among members of the network group to determine the structure of the network as well as the existence of cohesive sub-groups within the main network community. In addition, we analyzed the network to ascertain which central figure(s) play(s) key roles in the social network community. Not only is the structural prestige of a person in the social network a clear indication of the stability of the network but also a signal of the importance of social ties in the diffusion of information throughout the social system. Finally, we examined the social network to observe underlying correlating factors that have influenced the structure of the network with time. The second case study also uses SNA graph theory to analyze the evolution of research collaborations of Information Assurance Education (IAE), Security Education (SE) and Cybersecurity Education (CSE) researchers in primarily an academic domain. One of the primary objectives of this case study is to use a dynamic approach to explain the static topological features of the researchers in the collaborative network using a time framework. Within the past few decades, scholars and experts from the government, academia, and businesses from various local, regional and international institutions have collaborated on research topics in IAE, SE and CSE. The goal of this study is to analyze the research collaborations of IAE (that includes SE and CSE) educators who have published work in IEEE and ACM publications and other venues indexed by the IEEE and ACM digital libraries between 1999 and 2013. Specifically, we examine the structure of co-authorship in the IEEE and ACM community using various social network analysis (SNA) techniques and SNA tools. Overall, our results revealed a weakly connected IEEE and ACM network. Further, we saw a moderate increase in research collaboration at both institutional and cross-institutional levels. The results also show that the most prolific institutions are educational institutions related with the military.