The Week in Vis 13 (Mon, Nov 26 – Fri, Nov 30): Graphs

by Mike Gleicher on November 23, 2018

Class Meetings
  • Mon, Nov 26 – Lecture/ICE:Graphs
  • Wed, Nov 28 – Lecture:Graph Layout
  • Fri, Nov 30 – NO CLASS
Week Deadlines

Hope everyone had a good Thanksgiving.

Last week, we spent a lecture on 3D, and then took a lecture to revisit issues of scaling as a way to think more about Design Challenge 3 (which is going on as we speak).

This week, we’ll talk about graphs – which is a big topic. And one you’ve already seen at least some of for DC2. In fact, The TreeVis.NET web site (which is a required “reading” for this week) was recommended on the DC2 assignment.

On Monday, we’ll talk about graphs in general, and we’ll do a little design puzzle to get us thinking about node link layout. Then on Wednesday we’ll talk more about node-link layout.

You may want to look at this week’s learning goals Learning Goals 13: Week 13 – Graphs.

Readings (due Mon, Nov 26 – preferably before class)

Finding appropriate readings is suprisingly hard.

  • Arrange Networks and Trees (Chapter 9 from Munzner’s Visualization Analysis & Design) (Munzner-09-ArrangeNetworks.pdf 0.9mb)
  • TreeVis.net has a huge number of visualizations of trees. Look at the pictures and try to get a sense of how many different ways there are to do this.

Tamara Munzner gave a talk that gets across the point that there are many ways to show a graph. It gets the point across that there are lots of design choices and options. Plus, you’ll get a sense of the person behind the book (although, this was almost a decade ago). But, sitting through the hour is a bit much – so it’s OK to just watch a little bit and read through the slides.

  • Tamara Munzner. 15 Views of a Node-Link Graph: An InfoVis Portfolio, Google TechTalks, Mountain View CA, 6/06. Talk video (Video on YouTube) (slides)

I wanted to find a survey paper that covered the more computational aspects (the layout algorithms). I haven’t found one that I like. Instead, I am recommending this paper. Read it to get a sense of what the basic methods are – don’t try to get at all the details and subproblems and …

  • von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J. J., Fekete, J.-D., & Fellner, D. W. (2011). Visual Analysis of Large Graphs: State-of-the-Art and Future Research Challenges. Computer Graphics Forum, 30(6). doi:10.1111/j.1467-8659.2011.01898.x (official version) (authors’s copy)

Optional

There is a lot out there. One good general source for background is the book “Handbook of graph drawing and visualization” – which you can find drafts of the chapters online. In particular, the Chapter on Force-Directed Layout (at least the beginning parts of it) gives a review of the classical algorithms.

  • Kobourov, S. (2016). Force-Directed Drawing Algorithms. In Handbook of Graph Drawing (pp. 383–408). (pdf online)

For a modern algorithm for small to medium graphs:

DC3 Phase 1 “Extension”

by Mike Gleicher on November 16, 2018

As I mentioned in class Wednesday (the benefit for those who came on time!) – there is a no-cost extension for Phase 1 of DC3 (nominally due today) for a week. However, next Friday is a hard deadline.

We’ll talk about DC3 in class on Wednesday

The Week in Vis 12 (Mon, Nov 19 – Fri, Nov 23): 3D

by Mike Gleicher on November 16, 2018

Class Meetings
  • Mon, Nov 19 – Lecture:3D
  • Wed, Nov 21 – DC3 and Assorted Topics
  • Fri, Nov 23 – No Class:Thanksgiving
Week Deadlines

Last week, we took a detour and talked about dealing with high dimensional data. I didn’t get through everything (I wanted to say more about how to look at embeddings, and show some examples of how to work with them). Maybe I’ll bring those back – it’s a favorite topic of mine.

We also had the demo session for DC2. Thanks to all who participated!

The topics you may have expected to hear about – uncertainty visualization and visualizing models (such as machine learning) we’ll get to later in the semester.

Next week is Thanksgiving week. I understand that many people travel for the holidays. If you miss class Wednesday, be sure to talk to someone who doesn’t. Note that the usual discussion and seek and find are happening, so you may want to do it before you leave for the holidays (if you’re leaving).

On Monday, I’ll talk about 3D – which could be a whole course unto itself. There is so much to talk about. We’ll be focusing on the perceptual issues.

On Wednesday, I expect it to be a grab back. I suspect there will be more 3D stuff to wrap up. I was toying with talking a bit about Computer Graphics (how computers draw 3D fast), but instead I might talk more about embeddings since there’s so much more to say. But, the biggest thing is that I plan to discuss Design Challenge 3 a bit.

Graphs were moved to the week after Thanksgiving.

You may want to look at this week’s learning goals Learning Goals 12: Week 12 – 3D.

Readings (due Mon, Nov 19 – preferably before class)

It’s hard to know where to start. But for required readings, we’ll focus on the perceptual issues.

Optional

More resources on these topics are on the readings page from 2 years ago. All of the links should work for you, except for the Illustration handbook (which may be my favorite):

Demo Details for DC2 Demos

by Mike Gleicher on November 14, 2018

We encourage everyone to come to the demo session on Friday – you can learn a lot by seeing what other people have done!

For people who volunteered to do demos (the 15 people/groups who did):

  • These demos are during the class time slot (11-12:15) on Friday, November 16th.
  • We will be in 312 Wendt (this is the normal M/W room). Not 311 (the usual Friday room). 312 is a much better room.
  • The room has an HDMI connection. I will have an HDMI cable. I also have a mini-displayport to HDMI adapter. If you need another adapter, please bring it.
  • If your demo runs on the web, you can use my laptop if you like. (running Chrome). I will not install software on my laptop.
  • I will have some new data sets from real classes for you to try out. You don’t have to try them – but you can.
  • The timing is tight – 15 demos in 75 minutes. Everyone gets 5 minutes (on average). So please try to be efficient.

 

No Discussion for Week 11

by Mike Gleicher on November 12, 2018

In class, we ended up talking about high dimensional data and dimensionality reduction. Lectures connected to the readings will be rescheduled.

Coincidentally, I forgot to write a discussion prompt.

So, this week, we’ll have a “week off” from discussions. There’s nothing to discuss!

DC2 Demos Survey

by Mike Gleicher on November 11, 2018

Please do the following survey – whether you want to do a demo or not (it’s a way to let us know):

https://go.wisc.edu/0ds4wi

(this should take you to a google form)

Please do this survey on Monday, November 12th.

Class Meetings
  • Mon, Nov 12 – Lecture:High-Dimensions
  • Wed, Nov 14 – Lecture:Modeling
  • Fri, Nov 16 – OPT:DC2 Demos
Week Deadlines

Last week, we talked about the challenges of scale. But we only really got to talk about reducing the number of items. We didn’t deal with the problems of having too many dimensions. And also a lot of DC2 work happened.

This week, we’ll spend more time talking about scale: starting with the problem of dealing with too many dimensions. The discussion of scale will lead naturally to the discussion of modeling and uncertainty: basically, models tend to be summaries.

One change for this week: I am going to try to better integrate ICEs into the regular lectures, since otherwise the lectures become too much of monologues.

Also, we will do DC2 demos – stay tuned for details. But basically, we’ll have class Friday (in 312, not 311) where we’ll give people the chance to show off their projects. Unfortunately, with a 60 student class, we cannot do all the demos in one class period – we’re still working out a plan.

You may want to look at this week’s learning goals Learning Goals 11: Week 11 – Uncertainty and Modeling.

Readings (due Mon, Nov 12 – preferably before class)

While uncertainty is a critical topic, there is no obvious good reading for it. Last year I gave a whole long list.

This year, I want you to read two:

  1. Boukhelifa, N., & Duke, D. J. (2009). Uncertainty visualization: why might it fail? In Proceedings of the 27th international conference extended abstracts on Human factors in computing systems – CHI EA ’09 (p. 4051). New York, New York, USA: ACM Press. doi:10.1145/1520340.1520616 (ACM – free access on campus or using UW library proxy).
  2. Correll, M., & Gleicher, M. (2014). Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2142–2151. doi:10.1109/TVCG.2014.2346298 (web)

The point of uncertainty is that it means there is something in the data that is too hard for us to measure/model. Which makes a nice segway to considering modeling (since uncertainty usually involves a model), which is a hot topic because of that’s what Machine Learning is all about. This paper is particularly relevant if you are interested in the connection between Vis and Machine Learning (or Data Science more generally).

  1. M. Gleicher. A Framework for Considering Comprehensibility in Modeling. Big Data 4(2), June 2016. (page with PDF)

Optional

If you want to read more on uncertainty, I recommend:

  • Cumming, G., & Finch, S. (n.d.). Inference by eye: confidence intervals and how to read pictures of data. The American Psychologist, 60(2), 170–80. doi:10.1037/0003-066X.60.2.170 (pdf)

The statisticians have a lot to say about how we should think about uncertainty, especially in experiments. This paper gets at many of the issues (it is statisticians explaining to psychologists what they should do).

Last minute office hour tomorrow (Friday 11/9, 11am)

by Mike Gleicher on November 8, 2018

I will hold an extra office hour tomorrow (Friday, November 9th) during “class time” (11-noon).

I am happy to talk to people about DC2 designs, or anything else Vis related.

For example, if you have interest in a Vis beyond the class, please come talk to me!

DC1 Grading – Additional Information

by Mike Gleicher on November 7, 2018

Because of how course grading works, we had to give everyone a specific “letter” grade (A,AB, …). However, we are aware of things that are borderline, and can take that into account at the end of the semester when we assign final grades.

If you feel like your assignment was graded incorrectly, you may ask us to regrade it. We need this request before noon on Friday, November 9th. If we regrade your assignment, we will do a complete reassessment – your grade may go down (since we may find flaws we missed).

DC2 Phase 3 Feedback and General Tips

by Mike Gleicher on November 3, 2018

This started out as a list of replies to specific DC3s, but seeing that someone else got this feedback might be useful for you to help think about your assignment. Some of these things are general things that didn’t apply to any assignment in particular, but I thought of while looking over the assignments.

In many of the assignment responses, I pointed to a specific number – that means I thought that the comment (in the list below) is particularly appropriate. But, you might want to read through the list because more of these may apply to you than I was thinking at the time.

  1. The sample data may not have interesting enough things going on with it to show off your design. You may need to generate your own sample data. (you can use my sample data generator as a starting point, or make data by hand). If you make some interesting sample data and are willing to share it, let me know. If you provide good example data that others can use, we’ll reward you (part of your assignment can be creating sample data – but really only if its good example data, and provided early enough that it is useful for others).
  2. At demo time, we will have some additional data that may be more interesting to try things out with. However, it may not be interesting in the ways that you want.
  3. Providing standard chart types can be an effective way to answer some of the simpler tasks. We were hoping that people would think of richer tasks that require showing the structure/relationships (which are hard to show in standard charts), and use this to motivate non-standard charts.
    But, if you make standard charts, be sure to describe why they are appropriate and why the ones you made are well-adapted to the questions you are asking. Questions with simple answers (e.g., “which group has the most messages”) may not need of a visualization, questions that dump simple data (e.g., “what are numbers of messages in each group” as a bar chart) are unlikely to be considered as great assignments. A simple chart type might be a really appropriate design for an interesting question – but it would probably need to be coupled with some interesting data transformation.
    Combining sets of simple charts in interesting ways (where they are used together) can become a non-simple chart.
  4. In some cases, people said they are making a design, but said little about what the design is so we can’t comment on it.
  5. Since last year’s data was anonymized, the actual dates don’t match up. (the whole semester was shifted). I think that the entire data file was shifted by the same amount (so you can see there are, for example, 2 deadlines per discussion assignment – but it could be that the friday deadline got shifted to tuesday or something like that). You can look at last year’s web site to see the structure of the assignments (it was different).
  6. Many people have discussed either trying to learn a new programming environment, or are exploring different libraries to use with an environment they already know.
    On one hand, I’d like to encourage this: since learning about available tools is a good thing.
    One the other hand, it can take away from the assignment: if you spend 4 weeks learning a new tool, you may not have much time to use it to make something that addresses the assignment. At an extreme case, learning to use an entire different programming modality (like learning Javascript and enough stuff to make something in D3 from scratch) is hard enough to get to make something simple in 4 weeks, never mind an an implementation that addresses the assignment well.
    This makes it hard to compare assignments: someone who came in as a D3 expert can probably make fancier things than someone who decided to learn everything from scratch. Or someone who focused their energy on making good designs with tools they know (even if its pen and paper) will succeed differently than someone who spent weeks doing a thorough evaluation of different python libraries to choose an appropriate one, and only had limited time to come up with good designs.
    This leads to #7…
  7. If you spent a large amount of time learning new tools, please explain this in your project description. (see #6).
    This might be anything from “I learned Javascript” or “I did a thorough evaluation of 7 different Python graphics libraries to choose an appropriate one.” Give us a sense of what you learned or found (if you did evaluate a lot of libraries you can tell us why you chose what you did). We will take this into account when evaluating assignments (although, the assignment is really about creating effective designs, so that is the focus). I’m not sure exactly how this will factor in, but we will consider it. If you can convince us you learned something beyond what shows in the result, we will find a way to account for that.
  8. We only gave comments to the partner who submitted.
  9. In making a good design, but sure to consider what the design is good for – in your examples be sure to point it out. This probably means picking good examples (see #1)
  10. Remember, you need to have at least 2 designs. It could be one system that shows multiple designs.
  11. I have seen some nice looking versions of standard designs (e.g., graph views) – but think about how to adapt it to the specifics of the assignments and what tasks it address.
  12. Even if you provide something that makes it easy for us to run your code (a web page, github repo, executable), we probably prefer a demo anyway – that way you can show it off.
  13. Even if you deploy by GitHub (GitHub pages are a cool deployment mode for web projects), please provide us with a ZIP file of the code (easy – just have GitHub make the ZIP)
  14. Providing contrasts between designs (and between your designs and the baselines) can be a good way to help us appreciate your designs. Your designs should be “better” than the baselines (for at least some tasks).
  15. Think about (and explain) why the visualization adds value over simply giving a number or answer to the specific question.
  16. You can “fake” interactivity in a sketch by describing it and providing a sequence of images (or using illustrations that show off interactivity). You can try a mockup tool (like Adobe XD) to make an interactive sketch. You can “implement” interactivity using links in web pages or even power point (in the old days, there was this thing called “HyperCard” that was great for this).
  17. Unlike DC1, the goal is not to provide a visualization of a specific data set, but rather, to provide designs that can be applied to many data sets. You may illustrate your design on 1 data set, but consider that the design should be applicable.
  18. While you can generate data (#1), remember that you can’t generate different types of data (for example, you should not assume you can get access to the texts of the messages).
  19. We will have more instructions on demos next week, including some process to help people decide whether or not they “need” or “want” to give them. Hopefully, we can look at submissions and give initial feedback as to the value of a demo.
  20. Yes, you may use my code as a starting point (for data generation and python assignment data wrangling). Be sure to give proper attribution. (that said, I’m not sure the code I’ve given out is all that useful)
  21. For the final, upload code in a ZIP file