2013 UMD CS NodeXL Kramida

University of Maryland Computer Science Class (CMSC734) Student Projects Put NodeXL to Work: Finding Insights in Diverse Networks

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Graduate students in Computer Science at the University of Maryland in a class on information visualization produced a striking variety of NodeXL network analysis visualizations for their recent homework projects.  The class, taught by Prof. Ben Shneiderman (www.cs.umd.edu/~ben), covers commercial tools, such as Spotfire and Tableau, and research software, giving students a chance to learn a range of existing visualization techniques and tools.  The NodeXL homework project is done by individual students, midway in the semester, while 5-person student teams are also busy working on their major term projects, which create novel visualization tools for specialized applications.  To see all the projects, click:


(Don’t be deterred by security warnings, the class wiki is open for all to read, but only students can edit)!

Several of the 30 projects deal with Facebook, Twitter, email, Wikipedia, and YouTube social networks, with academic citation patterns and sports networks adding variety.  Entertainment, finance and medical analyses round out the collection, showing the huge range of potential NodeXL applications.  Students had only two weeks to find data, import it, clean it, and then create meaningful visualizations that enabled them to find interesting insights into connected structures.

Gregory Kramida’s analysis of stock symbol co-occurrences in financial articles

Gregory Kramida analyzed the connections among company names in the business press.  See:


The project shows the strong linkages between technology companies and consumer services, finance and public utilities.  The data set of more than 50,000  financial articles had more than 400,000 co-occurrences of stock ticker symbols.   He used the NodeXL grouping feature to organize the stocks into groups by industry and then showed results using the Group-in-a-Box layout feature.  This network is limited to companies that were mentioned together at least 50 times.


Ruofei Du’s analysis of co-authorship patterns

Ruofei Du probed the relationship among authors in 1033 scientific papers from the 1988 to 2013 User Interface Software & Technology (UIST) conference. See:


The co-author collaborations followed commonly seen patterns of professors and their students, but the relationships between academia and industry showed novel patterns.  After grouping authors by their organizations, it is apparent that Microsoft is well-represented at this conference through numerous collaborations with universities.


Joshua Brule’s analysis of actor co-performance connections from the television series Firefly 

Joshua Brule created an intriguing story of television and film actors and actresses that emerges from analysis of ten actors from the cancelled television series Firefly. See:


The actors had few collaborations before appearing on the program, but many afterwards.  The carefully constructed bipartite network shows how ten actors collaborated in 38 films, television shows, or videogames.