First Steps to NetViz Nirvana: Evaluating Social Network Analysis with NodeXL
Social Network Analysis (SNA) has evolved as a popular, standard method for modeling meaningful, often hidden structural relationships in communities. Existing SNA tools often involve extensive pre-processing or intensive programming skills that can challenge practitioners and students alike. NodeXL, an open-source template for Microsoft Excel, offers a potentially low-barrier-to-entry framework for teaching and learning SNA. We present the findings of 2 user studies of 21 graduate students who engaged in SNA using NodeXL. We found NodeXL to be an effective tool for a diverse set of users, and significantly, a tightly integrated metrics/visualization tool that can spark insight and facilitate sense-making for students of SNA. Our presentation will focus on the unique features that made NodeXL learnable and usable. After a brief overview of the NodeXL tool, we will describe our research methodology, based on Multi-dimensional In-depth Long-term Case studies (MILCs), an approach that enables effective evaluations of complex visual analytics tools. We will discuss NetViz Nirvana, layout principles that can increase the readability and interpretative power of social network visualizations, and present a sample of visualizations produced by the students. Finally, we will offer lessons learned for educators, researchers, and developers of SNA tools such as NodeXL.
Before becoming a phD student at the University of Maryland’s iSchool, Elizabeth Bonsignore served as an operations engineer and signals/intelligence analyst for the Department of Defense. She holds master’s degrees in Computer Science (Naval Postgraduate School) and in Education (Boston University, Overseas Program). She is interested in applying her diverse professional experiences to the study of communities of learning; specifically, the ways these communities develop, the nature of member relationships, and the ways information and language is re/presented and re/appropriated through new media such as interactive narratives.
Cody Dunne is a PhD student in Computer Science at the University of Maryland. He holds a master’s degree in Computer Science from UMD. His research focuses on information visualization, specifically graph drawing. His dissertation research focuses on computing the readability of graph drawings and the application of network analysis techniques to real-world problems, including document citations networks, thesaurus category semantic orientation relationships, and computer network traffic.
- Elizabeth Bonsignore and Cody Dunne
- University of Maryland