Time and location: We meet Tuesdays 11:45-1:25, Thursdays 2:50-4:30 in Shillman Hall room 325.
Office Hours: Thursdays from 1:30pm - 2:30pm in 302F WVH, or by appointment.
Network data structures have been used extensively for modeling entities and their relationships across such diverse disciplines as computer science, medical informatics, sociology, urban planning, and archaeology. Analyzing networks involves understanding the complex relationships between entities as well as any metadata or statistics associated with them. By encoding this data as an information visualization, we are able to leverage the impressive human visual bandwidth so that users can spot clusters, gaps, trends, outliers within a fraction of a second. These techniques can help experts interpret and explore complex networks, as well as gain confidence and algorithmic results. Moreover, visualizations are highly effective tools for communicating with other analysts or stakeholders.
This course will cover the principles of information visualization in the specific context of network science. It will introduce students to visually encoding data and our understanding of human vision and perception; interaction principles including filtering, pivoting, aggregation; and both quantitative and human subjects evaluation techniques. It will cover visualization techniques for several types of networks, including multivariate networks with attributes for entities and relationships, evolving and dynamic networks that change over time, heterogeneous networks consisting of multiple types of entities, and geospatial networks. Students will learn about the design of layout algorithms for node-link and matrix visualizations.
Examples in the course will be drawn from several domains such as visualizing concepts in medical records, the spread of infectious diseases, citations in academic literature, interactions of people and organizations, relationships in archaeological dig sites, news term co-occurrence, and computer network traffic flow.
Students will be expected to:
Read current literature
Critique existing visualizations
Use popular tools such as NodeXL, Gephi, Cytoscape, GLEAMvis, and NetworkX to visualize and analyze network data
Conduct a major team project to design and build an interactive visualization tool to address a real world network science problem.
Enrollment is limited to 3rd/4th/5th-year CS/DS/IS students and CS/DS/NetSci graduate students, except with permission of the instructor. No specific courses are required, but students are expected to have a programming background and basic CS knowledge for the course project. Students should be comfortable learning a new language on other own if necessary, JS/HTML in particular would be useful.
This is a grad-level course. All course requirements are planned to take you 10 hours a week. Please inform the instructor if the workload is light/heavy.
Each class will be 1 hour, 40 minutes long with a brief break. They will consist of instructor presentations, student presentations, paper discussions, and project activities.
Class and Online Participation
Students are expected to participate with in-class discussions, reading and lecture discussions on the Piazza discussion group, and answering other student’s questions on Piazza.
Assigned and recommended reading will be listed in the course schedule.
There will be several homework assignments over the course of the semester
Students will work in small teams to create interactive network visualization tools to solve real-world problems. Students will be expected to form teams, find client users (with instructor help), submit project proposals, present the results in class, create a short video, and write an academic article as if targeting a workshop or conference submission. Students are strongly encouraged to post their source code in a publicly available github repo with an open source license.
We will be conducting at least two course evaluations.
The final grade will be composed of:
Homework assignments: 40%
Final project: 45%
There are no required textbooks to purchase – only free assigned readings.
None of these resources are required, but can serve as additional references:
Analyzing Social Media Networks with NodeXL by Derek Hansen et al.
Designing the User Interface by Shneiderman et al.
Design for Information by Isabel Meirelles
Information Visualization: Perception for Design by Colin Ware
The Visual Display of Quantitative Information by Edward Tufte
All homework and project related due dates are final and provided in the course schedule. No assignments will be accepted for credit after the deadline. If you have a verifiable medical condition or other special circumstances that interfere with your coursework please email the instructor as soon as possible.
Academic Integrity Policy
TLDR; Obey software license requirements, attribute all copied source code, do not copy or only paraphrase text from other authors.
A commitment to the principles of academic integrity is essential to the mission of Northeastern University. The promotion of independent and original scholarship ensures that students derive the most from their educational experience and their pursuit of knowledge. Academic dishonesty violates the most fundamental values of an intellectual community and undermines the achievements of the entire University.
For more information, please refer to the Academic Integrity Web page.
About the Instructor
Prof. Cody Dunne works at the intersection of information visualization, visual analytics, human-computer interaction, and computer science. Cody’s work focuses on making networks easier to analyze and share and the application of network analysis techniques to real-world problems. He joined Northeastern in 2016. Previously, he worked as a Research Scientist in the Cognitive Visualization Lab as part of IBM Research, Watson, and Watson Health.
Cody advised and contributed to the NodeXL project, an open source network visualization template for Microsoft Excel. He received his Ph.D. and M.S. degrees in computer science under Ben Shneiderman at the University of Maryland Human-Computer Interaction Lab in 2013 and 2009, respectively. He earned a B.A. degree in computer science and mathematics from Cornell College in 2007.