ANALYZING DATA VISUALIZATION METHODS FOR ACADEMIC SOCIAL NETWORKS

With the growing popularity of social networking sites, social network visualization is becoming increasingly more popular; these visualizations can help improve the user’s experience or can benefit marketing agencies by condensing important information to an easier format; however, the advantages of social network analysis are not limited to sites such as Twitter or Facebook. The objective of this thesis is to examine methods of effectively visualizing social networks and applying them to a smaller scale network within an academic environment with the ultimate goal of providing a tool for students. In this case, the academic network is compromised of professors and their research connections, namely their co-authors. In particular, this data can be used to help students find relevant research and publications within the research area of their advisor. Ultimately, two methods are analyzed with academic network data; the first uses pre-existing software, Graphviz, and the second creates an independent interface that uses aspects of other visualizations to create an alternate perspective. Both emphasize different aspects of the data and provide varying viewpoints