Visualizing Layered Networks—CS 7295: Special Topics in Data Visualization

Fall 2021 Syllabus

Instructor: Prof. Cody Dunne
Homepage: c.dunne.dev/cs7295f21/ (make sure to log in to Canvas)
Time and location: M/W 2:50 – 4:30 pm Eastern in Hurtig Hall 310 (no remote participation planned)

Welcome to CS 7295! Perhaps you are taking this course to fulfill a requirement, or maybe you’re simply interested in the *magical* world of network visualization and graph drawing. Either way, CS 7295 provides students with not only theoretical knowledge but also a chance to conduct their own research projects.

Office Hours and Contact Info

Post general questions as a Canvas discussion 💬; send personal questions by email to cody.g.dunne@gmail.com 📧. I will not answer messages in Teams.

🔗Make sure to include links to your Canvas submission, GitHub repo, or anything else relevant! It's hard to debug code without seeing it.

We encourage you to ask questions early and often when things are unclear.

Instructor: Prof. Cody Dunne
Email: cody.g.dunne@gmail.com
I will make my best effort to respond within 24 hours, excluding Sat./Sun. or hours outside 9:00 am – 6:00 pm.
Office hours: M 5:15 – 6:00 pm at https://c.dunne.dev/zoom
Fun fact: I like pretty pictures that tell you something about data!
Library Visualization Specialist: Kate Kryder, Research Data Analyst
Email: k.kryder@northeastern.edu
Office hours: In Zoom by appointment (schedule here).
Kate is happy to help with D3 debugging.
Fun fact: Students who visit me often refer to the D3 Reusable Chart Model as the "Cody code."

Course Description

Students will explore algorithms, techniques, and tools for visualizing layered networks as part of a project- and discussion-based course.

In a layered network, each node is assigned to exactly one layer and layers are aligned along parallel linear axes. Layered network visualizations are useful for understanding sequence or flow data, hierarchies, or multiple data classes and support human decision-making in, to name just a few, medicine, computer science, physics, and sociology. The key to a readable visualization is to ensure spatial layout is used effectively to show relationships between nodes. But creating an optimal layout based on human readability criteria—e.g., edge crossings, edge bendiness, contiguous groups—is an open challenge. Layout algorithms are usually heuristics that prioritize computational scalability over optimal readability. This course will cover traditional fast heuristic layout algorithms as well as mathematical optimization techniques for creating layouts that prioritize human readability over layout computation speed.

In addition to the specific focus on visualizing layered networks, students will also learn the basic principles of data visualization, human-computer interaction, and how to validate results with computational and human-subjects experiments.

Prerequisites

There are no prerequisite courses. Familiarity with topics such as data visualization, human-computer interaction (HCI), web development, databases, and data mining is very useful for the course but not a required prerequisite.

Don’t worry if you have no prior experience with programming languages like JavaScript (i.e., D3) and Python, particular tools, or web design in general. However, students should be comfortable with learning what they need for programming their projects independently — and asking for help early.

Textbooks and Resources

Exciting news: All our course materials are freely available online or through the library! Links to the reading PDFs will be posted in the schedule.

Recommended

Although not required, the following references are recommend to help expand on the visualization theory and skills discussed in class.

Graph Drawing and Network Visualization

Kaufmann and Wagner (2003) Drawing graphs: methods and models.
doi: 10.1007/3-540-44969-8 (EZproxy metadata, PDF)
Jünger & Mutzel (2004) Graph Drawing Software.
doi: 10.1007/978-3-642-18638-7 (EZproxy metadata, PDF)
Sugiyama (2002) Graph drawing and applications for software and knowledge engineers.
doi: 10.1142/4902 (EZproxy metadata and download links, Canvas PDF)
Tamassia (2013) Handbook on graph drawing and visualization
(publisher metadata, author’s metadata and PDFs)
Battista et al. (1994) Algorithms for drawing graphs: an annotated bibliography. Computational Geometry, doi: 10.1016/0925-7721(94)00014-X (EZproxy metadata, PDF)
Proceedings of the Graph Drawing and Network Visualization symposium (see Springer links)
http://www.graphdrawing.org/books.html

Visualization Generally

Visualization Analysis and Design (Links to an external site.) by Tamara Munzner (EZProxy metadata and PDF(Links to an external site.)).
Design for Information by Isabel Meirelles
The Functional Art: An introduction to information graphics and visualization by Alberto Cairo
Information Visualization: Perception for Design by Colin Ware
Interactive Data Visualization for the Web (2nd edition) (website) by Scott Murray. Note that Scott uses D3 v.4, which is out of date.
The Visual Display of Quantitative Information by Edward Tufte

Required Supplies

We will do a lot of visualization sketching and design iteration in this course. Always have the basics, so you can participate effectively:

At least five different colors of pens—e.g., Pilot G2 packs 31128 and 34405—or pencils (with sharpener).
Plenty of white or near-white drawing paper. E.g., Strathmore 25-005 or 400-2 drawing notebooks, heavyweight white index cards, or even 8.5x11" printer paper.

We specify paper, rather than digital tools, for design sketching, as we have found that digital drawing tools tend to limit the breadth of student creativity and slow down design iteration.

We will also do programming and writing activities. You should always have:

A laptop suitable for programming and its charger

If you do not have the required supplies when they are needed to participate your grade will be docked accordingly.

Course Components

As this is a 4-credit class, the Registrar says you should plan to spend approximately 8–10 hours outside of class on homework in addition to the 3.33 hours in class (i.e., 11.33 – 13.33 hours per week total).

Lectures + Activities

Some classes will be split into two parts: lecture and in-class activity. The lecture component will cover foundational topics, and the remainder of the class will be devoted to an in-class activity to further explore topics and skills relevant to the course topics. Activities will include tutorials, design critiques, paper discussions, in-class programming and data analysis, and student final project presentations. Lecture slides are available here, generally within a few days after class.

Class Participation

Students are expected to participate both in-class as well as online through the course’s discussion group. As the course is based heavily on in-class discussions and activities, student participation is essential to the course. Plus, when we all participate, it makes our time together that much more interesting!

Class attendance is required and a student’s grade will be negatively affected by absences and tardiness. Absences may be excused by the instructor with advance notice. Absences will only be approved after the fact in cases of unavoidable necessity (e.g., medical).

Readings

Assigned reading will be given to read in advance of some classes, and will also be part of some assignments. Readings will be listed in the Course Schedule online. They will be drawn from freely available online material.

Homework

The homework assignments are an individual assessment and should not be completed in groups. This will include serving as the presenter and discussion lead for several readings.

Final Project

Over the course of the semester, students in the class will work together on meaningful and publishable projects. The projects will involve real problems and data and the instructor will provide frequent feedback throughout the semester to guide the students to publishable results. The instructor has several prepared projects that students can choose from—though students can propose their own ideas as well—that are well-situated for success. The instructor has already explored relevant literature, identified domain collaborators and preliminary datasets, and even has initial visualization designs in some cases for students to build upon. There will be mandatory project deadlines and associated assignments throughout the semester. Some of the project assignments will be done individually, and others in small groups. Students will be required to produce research products such as software, websites, or user study designs; a demo video; an in-class presentation; and a research paper. More instructions will be provided in the initial project assignments.

See more details of the project in the Project Overview (to be added).

Final Grade

Grades will be posted on Canvas with a running total grade weighted by assignment type as detailed on Canvas at the bottom or side of the Course Schedule. Final letter grades will be based on this weighted total using this scheme:

Name:	Range:
A	100%	to	93%
A-	< 93%	to	90%
B+	< 90%	to	87%
B	< 87%	to	83%
B-	< 83%	to	80%
C+	< 80%	to	77%
C	< 77%	to	73%
C-	< 73%	to	70%
F	< 70%	to	0%

There will be no rounding. E.g., 92.999% is still less than 93% and thus an A-. This is to encourage fair grading by reducing the effect of emotional appeals to the instructor, which not all students would feel comfortable making.

In general, the term "reduction of one letter grade" refers only to the letter and not the +/-. E.g., an A would become a B and not an A-. Likewise for reductions of more than one letter grade.

Changes to the final course grade must be requested in an email to the instructor within one week of receiving the grade. The request should contain a technical explanation of why re-grading is necessary. If the instructor approves the request, all work including assignments, participation, and the project will be re-graded to determine the new grade. The new grade can potentially be lower than the original grade.

There will be no pass/fail grading this Fall, even if Northeastern switches to entirely online instruction!

For details about individual assignment grading, rather than the final course grade, see Course Policies below.

Course Policies

Late Policy

All assignment due dates are final. Some assignments that require timeliness (e.g., in-class Reading Presentations and Project presentations) will be graded 0 if you are late. Other assignments will be accepted for 2/3 credit for [0, 9] hours after the deadline and 1/3 credit for (9, 24] hours after the deadline. The late policy is implemented automatically by Canvas, so 11:59:01 pm would still be considered late for an 11:59 pm 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 to discuss your options.

If you have a major conference/journal paper deadline or conference presentation that interferes with your coursework, email the instructor at least three weeks in advance to discuss your options. We will not provide late policy waivers for such cases with less notice.

Assignment Submission Instructions

On all assignments you are expected to follow the submission instructions exactly. If you don't, you will lose points accordingly.

Assignment Regrading Policy

Re-grading requests must be made by an email to cody.g.dunne@gmail.com 📧 within one week of receiving the grade. The request must include:

The subject line prefixed by "CS 7295 F21 — Regrade Request".
A link to your Submission Details page for the assignment (Canvas documentation on how to get to it) (if relevant).
The details on what aspect you're requesting to be regraded.
A short (1-2 sentences) explanation of why re-grading is necessary.

The new grade can potentially be lower than the original grade.

Changes to the Syllabus, Schedule, and Grading

The course syllabus, schedule, grading schemes, and grading rubrics are working documents that are subject to change throughout the entirety of the semester. Refer back to the "Home" page at any time for the most up to date document. We will make our best efforts to communicate any meaningful changes promptly. In general, we'll ask ourselves "could this change create an unpleasant surprise for a student who read the syllabus?" If so, we'll announce the change.

🐛 Bonus Points for Bug Bounties 🐛

If you identify problems or points of confusion with the course assignments, you may receive bonus points! In order to get the bonus you must:

Report the bug at least 4 days prior to the due date of the assignment by email to the instructor.
The email must:
- Have the subject line prefixed by "CS 7295 F21 — Bug Bounty".
- Provide a hyperlink to the assignment/web page/repo/document/file in question.
- Include the problematic excerpt, detailed information about what is missing, or other problem details.
- If applicable, suggest a fix including replacement text.

Points will be awarded by degree of severity as determined by the instructor. Generally expect:

- As intended — issues we will not fix: +0 points
- Minor — spelling/grammar/simple writing issues: +1 point
- Major — points of moderate confusion that students could work around with some effort: +2 points
- Severe — bugs that would block a large portion of students from completing the assignment: +3 points

Bonus amounts will vary at the instructors discretion. If multiple students report the bug, the student submitting the first report and the student submitting the most complete fix will get the most points.

Bonus points can increase the score of an assignment above the maximum and not just compensate for lost points.

We will never intentionally introduce bugs into the course materials.

Other Bonus Points

Some assignments may include other options for bonus points.

Communication Policy

Students can reach us by creating a new Canvas Discussion or sending an email. Emails are only appropriate for individual questions — e.g., medical leaves, regrading requests, meeting requests — or when otherwise directed. All general course and assignment questions should be posted as a Canvas Discussion so all students can comment and see the answers. Students that email on issues obviously of general interest will be referred to make a post on Canvas instead.

Help us help you! Always include the relevant links. If you’re emailing about a particular assignment, please include the URL of any relevant materials for us to help you. I.e., before submission send us a link to your GitHub repo — it's hard to debug code without seeing it — or other draft submission. After submission send us a link to your Submission Details page for the assignment (Canvas documentation on how to get to it). If you are asking a project-related question and already have a group, give us your group number too.

We will make our best efforts to respond within 24 hours, excluding weekends.

Classroom Electronics Policy

Students will be required to bring a laptop to class (see above). However, students are prohibited from using electronics during class except when specifically instructed for in-class activities.

Students are prohibited from using electronics for non-class purposes during class due to their distracting nature for the student as well as others. Example prohibitions include browsing Facebook, doing work for other courses, messaging friends, and watching sports.

Students are prohibited from using electronics during class for class-related purposes which are not relevant to the in-class activity. E.g., no working on out-of-class assignments during class.

Students who are observed violating the electronics policy will be asked to leave for the day and will be marked as absent. Any in-class grades for the day will be marked 0.

Any exceptions to the electronics policy, e.g., for medical monitoring, must be approved by the instructor in advance and in writing.

Academic Integrity Policy

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.

In this course you are expected to do each assignment independently unless otherwise instructed.

Plagiarism of any kind will not be tolerated, including but not limited to copying text, source code, images, video, visualization designs, and slides.

We will review your submissions against existing publications, designs, course submissions, and source repositories to ensure academic integrity. We maintain a repository of deliverables from previous instantiations of this and related courses to compare submissions against.

For more information, please refer to the Academic Integrity Web page.

Here are some key points to keep in mind:

Acknowledge where code, text, images, designs, etc. come from.
If you look at code online but are supposed to write something yourself, you can't copy the code, either by copy and paste or by writing manually. Just copying and manually renaming variables is definitely cheating. You can get the idea, but not the code.
Anything copied from bl.ocks.org, Observable, or Stack Overflow must be acknowledged. It is not considered just D3 documentation.
Copying *anything* without proper acknowledgement, even just one sentence or figure, is considered plagiarism.
Paraphrasing text closely without citation is plagiarism!
In your writing, citing the source alone is not enough — you must also surround any copied text with double quotes ("").
- Individual work is just that: individual. No code should be exchanged (via whiteboard, email, or over the phone). You probably shouldn't even discuss the assignment with others (but that isn't a strict rule).
- You have teammates for the group project. Thus you have no reason to collaborate with classmates outside of your team.
- If you are stuck; ask for help. Cheating is never the viable solution.
- Lying about participating in service counts as an academic integrity violation. And they tell us attendance anyway, so why would you?
- Why the heck are you in university if you aren't trying to learn? You don't learn by cheating (and, frankly, it can seriously ruin your career far more than failing a class).
- The minimum consequence for an academic integrity violation is a score of 0 on the assignment, reduction of the final letter grade by one (e.g., A- to B-), and reporting to OSCCR and Khoury College. Repeat violations will result in failing the course.

Regret Clause 🤦. If you commit some act that violates the academic integrity policy but bring it to the attention of the instructor within 72 hours, we may still give you a 0 grade for the relevant work submitted, but will not report the violation to OSCCR or Khoury College (except in cases of repeated violations).

Title IX

Title IX of the Education Amendments of 1972 protects individuals from sex or gender-based discrimination, including discrimination based on gender-identity, in educational programs and activities that receive federal financial assistance.

Northeastern’s Title IX Policy prohibits Prohibited Offenses, which are defined as sexual harassment, sexual assault, relationship or domestic violence, and stalking. The Title IX Policy applies to the entire community, including students, faculty and staff of all gender identities.

If you or someone you know has been a survivor of a Prohibited Offense, confidential support and guidance can be found through University Health and Counseling Services staff and the Center for Spiritual Dialogue and Service clergy members. By law, those employees are not required to report allegations of sex or gender-based discrimination to the University.

Alleged violations can be reported non-confidentially to the Title IX Coordinator within The Office for Gender Equity and Compliance at: titleix@northeastern.edu and/or through NUPD (Emergency 617.373.3333; Non-Emergency 617.373.2121). Reporting Prohibited Offenses to NUPD does NOT commit the victim/affected party to future legal action.

Faculty members are considered "responsible employees" at Northeastern University, meaning they are required to report all allegations of sex or gender-based discrimination to the Title IX Coordinator.

In case of an emergency, please call 911.

Please visit www.northeastern.edu/ouec for a complete list of reporting options and resources both on- and off-campus.