A number of instructors have been using NodeXL to help teach social network analysis. It is relatively easy to use compared to many other network analysis and visualization tools, while still providing a rich set of metrics and visualization features. It is also free and integrates with the already familiar Excel (versions 2007 and more recent). This page is meant to collect information that can be useful in teaching with NodeXL. Please email us (firstname.lastname@example.org) if you have been using NodeXL in your teaching or have resources that may be helpful to other instructors.
NodeXL Student Discount
There is a student discount for the Pro Version of NodeXL, which provides students with access to all of NodeXL’s features including social media importers (e.g., from Twitter, Facebook). Talk to Marc Smith (email@example.com) about the installation and payment process. The free version of NodeXL can be used to analyze and visualize existing networks, though it is limited in some of its features.
Analyzing Social Media Networks with NodeXL Insights from a Connected World – Version 2
An updated version of the most comprehensive NodeXL text authored by Derek Hansen, Ben Shneiderman, Marc Smith, and Itai Himelboim is being released Fall of 2019. The book is titled Analyzing Social Media Networks with NodeXL: Insights from a Connected World – 2nd Edition. The book includes 3 parts: Part 1 introduces social media and network analysis, Part 2 is a tutorial introducing core network concepts while walking through the major features of NodeXL, and Part 3 is a collection of case studies written by various network experts analyzing different social media tools (e.g., email, threaded conversations, Facebook, Twitter, YouTube, Wikipedia). The 2nd edition of the book covers many new features of NodeXL not found in the old book. The datasets used in the 2nd edition, as well as the 1st edition are included below.
2nd Edition Book Files
NodeXL Sample Files: After downloading these files, make sure to use the Import –> “From NodeXL Workbook Created on Another Computer” to open the files. This will create your own copy of the file and make sure it works in the current version of the software.
- ABCD_Network_Data – The raw data for the fictional ABCD company.
- ABCD_Network_Data_endofchapter4 – ABCD Network data after chapter 4 tutorial
- ABCD_Network_Data_with_endofchapter5 – ABCD Network after chapter 5 tutorial
- ABCD_Network_Data_with_endofchapter6 – ABCD Network after chapter 6 tutorial
- Marvel_Movie_to_Character_Raw – Dataset connecting Marvel Cinematic Universe characters to movies they appeared in.
- Marvel_Movie_to_Character_Completed – Movie-to-Character network after tutorial in Chapter 6 (see Figure 6.5 in book).
- Marvel_Affiliation_Matrix_Example – Excel spreadsheet showing how to convert the movie-to-character network into character-to-character and movie-to-movie networks.
- Marvel_Movie_to_Character_CompletedXY_movies – Movies mapped along the X and Y coordinates (see Figure 6.6 in book).
- Marvel_Movie_to_Character_CompletedXY_characters – Characters mapped along the X and Y coordinates.
- CSCW_2018_Twitter_Raw CSCW_endofchapter6 – CSCW 2018 Conference Twitter dataset after tutorial in chapter 6.
- CSCW_endofchapter7 – CSCW 2018 Conference Twitter dataset after tutorial in chapter 7.
- Senate115 – C0-voting record for the 115th Senate raw data
- Senate115_after_chapter7 – C0-voting record for the 115th Senate after tutorial in chapter 7.
- TwitterGardasilSearch – Twitter Gardasil Search Network raw data
- TwitterGardasilWords – Twitter Gardasil Words Network raw data
- TwitterGardasilWords_After_Group_Analysis.xlsx – Twitter Gardasil Words Network after co-word analysis showing most popular words (see Figure 8.7)
- TwitterGardasilWords_After_Individual_Analysis – Twitter Gardasil Network after co-word analysis showing an ego-network of a single word (see Figure 8.8)
1st Edition Book Materials (no longer maintained)
Slides from Analyzing Social Media Networks with NodeXL: Insights from a Connected World (first edition) that include all images and their descriptions are below:
- Chapter 3 images
- Chapter 4 images
- Chapter 5 images
- Chapter 6 images
- Chapter 7 images
- Chapter 8 images
- Chapter 9 images
- Chapter 10 images
- Chapter 11 images
- Chapter 12 images
- Chapter 13 images
- Chapter 14 images
- Chapter 15 images
1st Edition Book Sample Files
- Kite Network dataset – the simple kite networks used to teach basic network metrics
- Serious Eats dataset – a multimodal network connecting people to blogs and/or forums
- US Senate 2007 dataset – the co-voting network of US Senators in 2007
- Les Miserable co-appearance network – the network of characters in Victor Hugo’s book based on their appearing in the same scenes together.
- css-d_email list network – a network of those posting to the css-d email list in Jan and Feb of 2007 (email addresses are anonymized)
- ABC-D_email list network – Discussion list network discussed in Chapter 9 of the book. Note that Eigenvector centrality is calculated slightly differently, as is Betweeness Centrality (which used to be normalized to the node with the highest score, but no longer is).
- Ravelry_Raw – Raw bimodal dataset of Ravelry users connected to 3 different discussion forum groups (discussed in Chapter 9 of book).
- Ravelry Completed – Ravelry dataset and completed visualization that matches Figure 9.10 in book.
- The following Enron Datasets are based on a subset of all available Enron email messages coded by researchers at the University of California at Berkeley (see
http://bailando.sims.berkeley.edu/enron_email.html). Slight variations in numbers of email messages may result from different ways of handling non-standard email messages.
- Enron_Dataset_Unfiltered – includes all 1,801 edges derived from work-related messages occurring later in the collection that discuss the California Energy Crisis.
- Enron_Dataset_FERC_only – includes subset of unfiltered dataset that includes the term FERC somewhere in the email message (this is the one analyzed in Chapter 8 of the book).
- Enron raw messages – Raw Enron messages that can be imported into NodeXL as described in Chapter 8 of the book)
- Sample Facebook Egonetwork or Sample Facebook Egonetwork with Metrics – An anonymized Facebook network with name pulled from the most common 2014 baby names dataset.
Other Files of Interest
- Serious Eats Affiliation Matrix Example – an Excel file that shows how to transform the bimodal Serious Eats data into two unimodal networks of people-to-people and forum-to-forum
Korean Language Tutorial on NodeXL
Students of Assistant Professor, Annie So Young YU, Hannam University have put together this tutorial: Visualizing Networks with NodeXL 101 in the Korean Language
A very large and growing collection of images and NodeXL files are uploaded to the NodeXL Graph Gallery. Click on the image you like and look for the Download as NodeXL file link at the bottom of the page. Many of these graphs show off NodeXL’s latest features which are not covered in the book.
Assignments based on NodeXL
Feel free to use and/or modify the assignments provided below with proper attribution. If you have your own assignments to share please send them to Derek Hansen at firstname.lastname@example.org
- Facebook assignment for beginners. (see IT 101_Facebook_Lab.docx). This assignment was created by Derek Hansen for BYU’s IT 101 students who have never been exposed to network analysis before. Students can complete it with some training in approximately 1-3 hours. Update: Unfortunately Facebook doesn’t allow capture of this data anymore, but a similar assignment could be used that is based on Twitter data or Facebook interaction data.
- Advanced visualization, analysis, and interpretation assignment. (see Social Network Analysis Homework Assignment.docx). This assignment was created by Derek Hansen for BYU’s IT 515R: Web and Social Media Analytics course. Students work on this for several weeks. Ideally they will first get feedback from peers and the instructor on the goals of their analysis and the data they’ll be using. Next they create a draft visualization that is shared with their classmates and the instructor for feedback. Finally, they present the results to the class and post them to the NodeXL Graph Gallery (after anonymizing data if it is private).
Selected Courses that Use NodeXL
- New Media (18060-01) Spring 2013, Fall 2014, Fall 2015, Spring 2016, Department of Library and Information Science, Hannam Universiry, Republic of Korea (South Korea). Instructor: So-Young YU. (seeMapping Social Media of Korean Brands – in Korean Language)
- Social Web: (Big) Data Mining (JSB454), Summer Semester 2014/2015. Charles University in Prague. Instructor: Jakub Ruzicka
- Social Media Analytics, Fall 2012, State University of New York at Buffalo. Instructor: Sanjukta Smith
- Web and Social Media Analytics (IT670), Winter 2012, Fall 2012, Winter 2014, Fall 2016, Brigham Young University’s Information Technology Program. Instructor: Derek Hansen
- Social Network Analysis for Engineers, Summer 2012, University of Tokyo’s School of Engineering. Instructor: Petr Matous
- Research Methods in Political Science (syllabus), Winter 2013, University of Trento, Department of Sociology and Social research. Instructors: Mario Diani, Katia Pilati, Elena Pavan
- Network Analysis of Social Media Data, Winter 2012, University of Georgia’s Grady College of Journalism and Mass Communication. Instructor: Itai Himelboim
- Communities of Practice (LBSC 708P), Spring 2009, Summer 2010, Spring 2011, University of Maryland’s iSchool. Instructor: Derek Hansen
- Complex Systems in Business (BMGT808L), Spring 2009, University of Maryland’s Smith School of Business. Instructor: Bill Rand
- Social Networks (SOC709), Fall 2009, University of Maryland’s Sociology. Instructor: Alan Neustadtl
- Social Computing and Web 2.0 (BUDT 758v), Fall 2009, University of Maryland’s Smith School of Business. Instructor: Xiaoqing Wang
- Social Networks (COM 380/580), Spring 2011, Illinois Institute of Technology’s Communication Department. Instructor: Libby Hemphill. Syllabus
- Group Processes (Sociology 419/519), Fall 2009, Ohio University, Instructor: Howard Welser
- Principles of Marketing and Retail / Distribution Management, University of Hawai‘i at Mānoa’s Shidler College of Business, Instructor: Jennifer D. Chandler
- Communication in Groups & Organizations, Carnegie Mellon University’s Tepper School of Business, Instructor: Robert Kraut
- Social Network Analysis (IEMS 341/COMM 395 & IEMS 441/COMM 525), Northwestern University’s Industrial Engineering & Management Science and Communication Studies, Instructor: Noshir Contractor (with graduate student Brian Keegan)
- Journalism in the Networked World (JOUR 390), Northwestern University’s Journalism School, Instructor: Rich Gordon (with graduate student Brian Keegan)
- Online Research Methods (DEMO8087), Australia National University’s Australian Demographics and Social Research Institute, Instructor: Robert Ackland
- Special Topic in Social Research (DEMO8081), Australia National University’s Australian Demographics and Social Research Institute, Instructor: Robert Ackland
- Six Degrees of Separation (UHON 350), Fall 2010, Southern Illinois University. Instructor: Scott McClurg.
- Project Strategy module course, VIA University College, Denmark. Instructor: Peter G. Harboe
- Data Analytics (LIS 7491), Winter 2011, Winter 2012, Wayne State’s School of Library and Information Science. Instructor: John H. Heinrichs
- Business Analytics (MIMS2010SBA), IE Business School in Madrid, Spain, Instructor: Jie Mein Goh
- Literature in a Wired World (ENGL278W), University of Maryland’s Department of English, Instructor: Marc Ruppel
- Information Visualization (ITCS 4121/5121), Spring 2011, University of North Carolina at Charlotte. Instructor: Jing Yang
- Social Networks – Theories and Applications (COMM3342), Spring 2011, University of Texas at Dallas. Instructor: Cuihua (Cindy) Shen.
- Current Trends in Social Computing (IS30290), Term 2, 2012-2013, UCD School of Information & Library Science. Instructor: Eric Cook.
- See Courses on Social Network Analysis for a list of Social Network Analysis courses more generally.
Selected NodeXL Publications
- See this Pinterest NodeXL Page for images from publications that use NodeXL through 2015 and links to the publications.
- Himelboim, I., McCreery, S., & Smith, M. (2013). Birds of a feather tweet together: Integrating network and content analyses to examine cross-ideology exposure on Twitter. Journal of Computer-Mediated Communication, 18(2), 40-60. DOI: 10.1111/jcc4.12001
- Bonsignore, EM, Dunne, C, Rotman, D, Smith, M, Capone, T, Hansen, DL, Shneiderman, B (2009). First Steps to NetViz Nirvana: Evaluating Social Network Analysis with NodeXL. International Symposium on Social Intelligence and Networking (SIN09), Aug 29-31, Vancouver, Canada.
- Smith, M, Shneiderman, B, Milic-Frayling, N, Rodrigues, E, Barash, V, Dunne, C, Capone, T, Perer, A, Gleave, E (April 2009). Analyzing (Social Media) Networks with NodeXL. Proc. Communities & Technologies Conference, Springer (June 2009).
- Hansen, DL (2011) Exploring social media relationships, 43-51. In On the Horizon 19 (1).
- Hansen, DL, Smith, MA, Shneiderman, B (2011) EventGraphs: Charting Collections of Conference Connections. In Forty-Forth Annual Hawaii International Conference on System Sciences (HICSS). Also see EventGraph SlideShare Presentation
- Hansen, DL, Shneiderman, B, Smith, MA (2010) Visualizing threaded conversation networks: mining message boards and email lists for actionable insights, 47-62. In Proc. Active Media Technology 2010, Lecture Notes in Computer Science 6335.
- Hansen, D, Rotman, D, Bonsignore, E, Milic-Frayling, N, Rodrigues, E, Smith, M, Shneiderman, B (Submitted). Do You Know the Way to SNA?: A Process Model for Analyzing and Visualizing Social Media Data. HCIL-2009-17 Tech Report.