
Linear Layout for Network Visualization (End of the era of network hairballs) - draegtun
http://mkweb.bcgsc.ca/linnet/
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JunkDNA
As someone who has spent _a lot_ of time staring at big hairball biological
networks, I've always been frustrated by them. I've been looking for a better
solution for some time now. I'm not sure this is it though.

My main beef is that I feel strongly that a visualization succeeds when you
don't have to explain how it works. Just looking at the main page, without
reading any of the slide deck, I had a hard time understanding what was going
on. In fact, for the first simple example in the slide deck, I found the
original network to be easier to grok than the new version. Things certainly
appear more _organized_ than when in a big hairball, but I still have trouble
extracting any meaningful information.

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nervechannel
That was my reaction -- I don't get it.

I liked his Circos package better: <http://mkweb.bcgsc.ca/circos/>

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igrekel
As you probably know, Martin Krzywinski is also the author of the Circos
software. Circos has been in development for much longer and addressed
different kinds of issues.

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nervechannel
Yes, that's why I referred to " _his_ Circos package"

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cscheid
"Only for networks? No"

That's because it looks like he reinvented Parallel Coordinates:

<http://peltiertech.com/Excel/Charts/ParallelCoord.html>

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jbl
This seems like a great method for ad hoc visualizations of multi-dimensional
and graph-structured data. I could see building a tool that allows a user to
pick a few dimensions and toggle between different orderings.

That said, I wish the authors had not used/brought up the notion of "visual
analytics". Visualizations are great exploratory and illustrative tools, but I
believe they leave much to be desired when it comes to doing the actual work
of analysis. When looking for explanations --- particularly in large datasets
--- is it not better to define some objective criteria which one can measure,
rather than relying on easily tricked visual cues?

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cscheid
You're right that it is a problem, but defining objective criteria before
knowing exactly what it is that you're looking for is potentially just as bad.
This is part of the reason folks like Tukey defended statistical graphics
early on: exploratory data analysis is tremendously important, and it's rare
that you would know exactly what you're looking for.

Also, this problem of making sure what you're seeing is actually in the data
is one that researchers are actively working on! There's a paper due to come
out at a top visualization conference on the relation between formal
hypothesis testing and visualizations which goes to the heart of your "easily
tricked visual cues" comment. It's not currently available (and I'm not a co-
author), but if you're interested, be on the lookout for "Graphical Inference
for InfoVis"

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jbl
Yep. Been there, done that. :-D

I didn't mean that exploratory analysis is not important, just that it's a
distinct part of data analysis and only the beginning.

That paper sounds cool. I'm looking forward to checking it out.

