North Carolina A&T State University's project entitled: Research Initiation Award - SONAS: Social Network Analysis and Simulation Systems, proposes to study different social networks and develop a web-based Social Network Analysis and Simulation (SONAS) system. The goals of this research are: to develop a social network analysis and simulation system, construct a large national input-output (IO) financial network based on Bureau of Economic Analysis data; utilize the Centers for Disease Control and Prevention National Health Statistics data to create a National Health Statistics Network; develop a network reduction algorithm to enhance the performance of the SONAS system; and investigate network dynamics by developing a self-evolving network environment.
The project will increase the understanding of social network properties of people and computers from both computational and social perspectives. The national health statistics network will lay a foundation for detailed analysis in matrix formulations with corresponding network flows. The merger of the national health network with the national economic network will be of importance not just for the network analysis in this project, but also to connect the continuous group theory, Markov theory, and network topology to a well-defined mathematical foundation and structure.
The educational objectives of the project are: to establish a modern Social Network Simulation Laboratory at North Carolina A&T State University; to recruit, train, and mentor motivated undergraduate students in social network analysis and simulations; and to attract underrepresented students to pursue a career in computer science.
In this project, the investigators systematically studied national economic social network and national health social network and develop a social network analysis and simulation system. We also studied the self-evolving dynamical network which allows us to see what types of networks, topologies, structures, clusters and other features can evolve from different sets of rules. Through seeking what types of rules give stable structures and how they evolve as well as how similar these systems are to the networks, it will provide a totally new environment for network evolution and allow us to study network dynamics from a rule-based system rather than to model the highly nonlinear aspects of network evolution. Our research provides extensive studies for not only the mathematical foundations of social networks but also empirical analyses. It increased the understanding of social network properties of people and computers from both computational and social perspectives. It provides new models of social networks involving massive numbers of humans. This project has broader impacts on the nation's higher education system and high-tech industries. The ability to conduct extensive analysis for social networks is needed by a wide variety of corporations, universities, hospitals, and government agencies. Similarly, theoretically and empirically validated means to analyze social networks in the simulation systems would benefit all social network community at large. The impact of this project also extended to academia through educational efforts, including students training, curriculum development, seminars, and outreach, which are integrated into a cohesive package that recruits, trains, and mentors motivated students in social network analysis and simulation systems, and attract underrepresented students to pursue a career in social network and computing. This educational objective has been achieved through the mentoring of undergraduate students, curriculum development of new courses in relation to the proposed research.
