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Visual Exploration of Complex Networks
jweebo writes "Seed magazine has a story on complexity, and how it can be visually represented with fascinating results. From the article:
'Complexity is everywhere. It's a structural and organizational principle that reaches almost every field imaginable, from genetics and social networks to food webs and stock markets ...Collected here are a few of the many intriguing, and often beautiful, images that illustrate how the whole is more than the sum of its parts.'"
Wow, a winamp visualization.
Religion for nerds. Stuff that really matters
I have a book, about a thousand pages long, by a certain author of a certain mathematics program (who I will not name here) that basically says the same thing.
Translation for the 1000+ pages:
"omGz)R patterns pwnz joO!"
Really though, the guy goes on and on about his 'new kind of science' and after a thousand pages gets pretty much nowhere.
But hey, it was complex, man! Serious!
TLF
I do not respond to cowards. Especially anonymous ones.
Looks like a rehash of a (Horizon/Equinox) documentary I saw in about 1983, or James Gleik's Chaos book from about the same time. Nothing (new) to see here, move along.
It's true I tell you, feller at work's next door neighbour read it in the paper.
This is nothing new. A picture of neurons, big deal. People are obviously more complex than their neurons, yet we don't give that any thought, why should I be amazed by seeing a small part of something with which I come in contact every day?
Send email from the afterlife! Write your e-will at Dead Man's Switch.
Seed magazine has a story on complexity, and how it can be visually represented with fascinating results.
I find that certain complex things are best represented as a series of tubes. Not a big truck that you can just dump something on, but a series of tubes.
The theory of relativity doesn't work right in Arkansas.
Must agree with limited crowd, so far. This is nothing new at all. Five somewhat pretty pictures. Seen much more elsewhere.
You've been reading "Fractal Geometry of Nature" by Benoit Mandelbrot. Very nice illustrations and the section on how fractals all started and another on fractal dimensions were good, but otherwise the book was far too vague and had few proofs. This demonstrates Heisenberg's Writing Principle, which states that you can either know bout a topic or write about it, but not at the same time.
It's a small world and it smells funny; I'd buy another if it wasn't for the money; Take back what I paid (SoM)
Yep, they're pretty.
... and what does it mean? What information is conveyed in this manner?
And they appear to be meaningless. Particularly the last one with Rammstein listed across from Britney Spears. Lots of coloured lines with lots of intersections
The article is a little short, I would have liked more more more!! :-)
May I suggest Information Architecture from Peter Morville. He is also co-founder and president of the Information Architecture Institute.
May I also suggest taking a look at Prefuse, an open source project to interactively vizualize organized information (still in beta however).
Animoog.org
Intriguing sure, beautiful
You can be an atheist and still not want to succumb to some weird cross-over sheep disease -- AC
""Seed magazine has a story on complexity, and how it can be visually represented with fascinating results."
[Slashdot]
Opinions expressed as facts.
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Reminds me a little to a documental I saw on tv like 10 years ago or so, the interpol was using a new system to correlate all known law offenders from databases in a boss to contact way, the results (although in only 2D) were most interesting.
...was a big field of study in the time period I obtained my PhD. I was sucked into the field along with many others. The study of complexity gave me a fast PhD ... but really ... there is no news here.
Wow, this is Unix! I know this!!
--Lex
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Something along similar lines is Frank van Ham's work on visualizing large state spaces. He's generated some neat visualizations of complex transition systems associated with various protocols.
Like someone else said, this article barely presents any information, and from what it sounds like, people here have seen this all before. Does anyone know of any software that can present this kind of interface for music files?
I judt got a nre Kinesis keybiartf so please excusr ant egregiou typos.
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pixels on the display: 2 million or so.
- insufficiency of the clustering algorithms: showing one pixel per node and random placement, or placement by DFS traversal? for trees, or for graphs where classification is the primary concern, then tree-map or "Csoft" views scale relatively well in this regard, but what about for more general problems?
- implementations (or algorithms) that don't scale: e.g. graphviz uses n^2 (n=#nodes) space for its graph layout!
one must always think about the summarization criteria: what aren't you going to show? how will you indicate that summarization has occured? how do you denote drill-down capability? what will the form of drill-down be? what heuristics should you use to selectively deaggregate, in order to highlight potentially interesting subgraphs? for large-scale info, this is as important as what you will be showing, and how it will be shown! for our stuff, we have graphs with tens of millions of nodes.http://www.visualcomplexity.com/
python -c "x='python -c %sx=%s; print x%%(chr(34),repr(x),chr(34))%s'; print x%(chr(34),repr(x),chr(34))"
Hack the Gibson
"Intriguing sure, beautiful ... huh ?"
A few beers will clear that right up.
It's here : http://infosthetics.com/ You can find there many more examples of visualization of complex sets of data. It's very interesting, and sometimes strangely beautiful. It is needed in biology for example, where any student can now easily check, in one or two days, the expression of all the genes in an organism (thanks to micro-arrays). If you want to make sense of these huge amounts of quantitative data, if you want to extract some important fine interaction that could be lost in the millions of numbers, you definitely have to use these kind of visualization tools nowadays.
The point of visualizing data is to learn something that you could not do with the raw data. In all of the cases shown in the article (yes, I acually read TFA), I didn't spot an example where it actually showed anything useful.
The first example with proteins: how similar are two proteins? If two shapes are similar (and please, how many proteins where being graphed there? One, two, five?), then you might be able to recognize it. If they are similar shapes, are they always presented in the same orientation in space? Does color have any meaning? Does this graph have any legend? If I gave someone who understood the graphs two proteins, what could he say besides "these are related" and "these are not related"? We already have wonderful programs to compare two proteins and say how similar they are two each other, along with being able to the estimate significance of the measurement.
I'm not sure that the other graphics look more informative. They are all pretty, but if they do not convey information (and not lose a large amount of relevant information), then they are just a nice way to generate patterns for some nerd's tie.
Dr. Wolfram (of Mathematica) offers PDFs of his book for free here (or pay $60 for hardcopy):
http://www.wolframscience.com/thebook.html
I do suggest you at least glance over the first few chapters, look at the pictures.
Also note that the guy got his PhD in Physics at the age of 18 I believe.
Obama likes poor people so much, he wants to make more of them.
This makes me think about the character Gentry from William Gibsons Mona Lisa Overdrive who was looking for the shape of the matrix.
From the article
"This region represents 'mainstream' music and includes many popular artists. Immediately adjacent is a set of points that represents 'indie' music. Thus, 'indie' music is not as independent as some might like to think."
For some more information on network visualization check out David Glecih's website http://www.stanford.edu/~dgleich/. He is one of the authors of the World of Music Paper mentioned in the paper. He has done a lot of work on visualizing high dimensional datasets. He has also done a lot of work on the computational side of complex network research. He released a number of Matlab packages that related to the subject. Also check out his artistic visualizations of the Flickr network http://www.stanford.edu/~dgleich/demos/visualizati ons/index.html.
"The point of visualizing data is to learn something that you could not do with the raw data"
The social patterns of slashdot.
I was working on diagramming complex systems (we called them "systems", not "networks" back then) 30 years ago. It was very interesting to see how the charts turned out. Also turned up some fascinating designs, as well as some other rather surprising effects.
Too bad no one was interested in it at the time. Not officially, at least.
...than a thousand words.
Really, you'd be amazed at how even the simplest graphical interpretation of complex data can really show up points of interest. And it's not difficult to see why: Humans' primary sense is visual and we have evolved some seriously complex neural algorithms to interpret visual data.
A simple graph is a case in point. Now take a large amount of complex data and apply just about any process you care to name to present a graphical representation and you can easily see the overall picture.
A very simple example which illustrates statistical clustering. Even with totally random numbers, you *will* find islands of apparently significant populations. This is a common counter-claim to action groups who claim, say, a correlation between mobile 'phone masts and incidents of child leukaemia*. Anyway:
Generate a stream of random numbers and assign a symbol for n = 0.5, display the symbols in a grid and, hey presto! Look at those clusters!
On a more positive note:
We often use graphical representation in our work. This ranges from CTK representations of molecules we're looking at (xlation - pretty pictures with balls and lines) to grid based colour indexed representation of multi-dimentional data sets. In each case the point is to present data in a way that we humans can quickly spot potential areas of interest and get a "feel" for the data we're looking at.
It's all good stuff. (Sometimes very pretty, too)
* Actually, this is a good example of why I'm always wary of purely statistical "proofs". In this case the *science* (ie. proposed mechanisms for this) don't hold up to current understanding.
As a net musician I found the visualization of connections between musical artists very interesting, and wish it was better explained. Interesting how pop seems to form one small cluster in the corner while everything else is wildly spread out. If one is aware of more than just mainstream schlock, one will recognize some fairly abstract names on the graph alongside mainstream performers. What I don't understand is the very bright path leading from Autechre to Yanni..!
Mandelbrot became famous for the visualizations of complexity/chaos of the fractal algorithms in his name. Mandelbrot was initially handicapped by a very immature computer graphics field in the 1970s- CRT pixel displays hadnt been invented yet beacuse memory cost too much. I recall his colleague Voss(?) at the time first rendered Mandelbrot diagrams on alphanumeric teletypes. A square array of characters was printed where the amount of blackness in each character would represent a pixel density. When Scientific American printed a column on Mandelbrot in 1979 they caused computer labs buy new, expensive graphics terminals ($30k) and paralyzed many a computer system.
I won't argue the "peer-reviewed" argument, you are definitely right on this (although, why should every little bit be scrutinized? Wolfram was attempting to show the connections between all of the work, not the individual pieces themselves). However, I don't think you are being fair to the issue of citings.
Had you read the book as you seem to indicate, you would know that on nearly every page of it Wolfram goes on (ad nauseum, it seems) about how he wasn't the first, telling you who originally did it, and wondering why they didn't see the connections (or why letter researchers didn't either). He notes that some did see some of the connections, but none seem to have seen all of the connections and implications. Finally, Wolfram provides copious footnotes and other references throughout the book that do reference others. The last part of the book (you know, the part that condenses about 1500 pages into 500 and is so dense as to make your eyes bleed) goes into even more detail and explanation, with still more footnotes and citings. He never once says "I invented all of this" - indeed, he mentions over and over again so many times how he is "shoulder standing", and upon who those are, that it is nauseating in its redundancy.
I am not saying this work is a perfect book - no book is. However, it is the kind of book that causes a lot of people, learned or not, to dismiss it outright because it "didn't go through proper channels". These kind of books/research generally wind up in one of two categories after a lot of time has passed: the crackpot/delusional bin, and the "OMFG! His research is right!" bin. You and many others seem to be pushing hard for this work to fall into the former. I personally believe it will fall into the latter.
Only time will tell, though...
Reason is the Path to God - Anon
I am not too sure what you mean by "wanking" - when I read it, I felt it was the opposite - that he was self-deprecating his research. He constantly refers to others research in the same areas, and notes copiously who they are/were. He does talk about how he feels he is the first to put 1+1 together, about how others have done similar research but never came to the conclusions he has. I haven't read all of the material out there on the varied and wide subjects, but I have read a lot (CA, complexity, emergence, chaos, etc) - and none of them ever came to the conclusions Wolfram has.
His basic conclusion - what the book goes over and over with, nearly "pounding" it into your head - is that complexity can and does arise in nature from simple sets of "rules". He found one of these sets (I think it was 6 rules for a 2D CA that he uses as an example throughout) using Mathematica, speculating that the six rules were the simplest he found, but by no means necessary the simplest possible. This simple set of rules, given certain input patterns, he shows how it can simulate the outputs of much of what we discern as natural "chaotic" processes - wind and water patterns, weather, seashell designs, zebra striping, and snowflakes - to name a few. Furthermore, the same system of rules can be coaxed into acting and working exactly like a UTM (Universal Turing Machine). Imagine - six simple rules which can replicate any other UTM (including the PC you are using right now, by the way, given enough time to run, of course). He goes to show that even when you make the system more complex (for example, by adding more rules or extra dimensions), slices through the data set show that the output devolves into the simpler two-dimensional six rule implementation. This is the essence of what he calls the "Theory of Universal Computation".
Yes, other authors disagree with him - including authors that I hold in high regard as well (Ray Kurzweil comes to mind). Who is right, who is wrong? No one really knows, but the code is simple to reproduce and play with (many have wrote implementations of it in many languages since the book's publication), so there is a foundation upon which to explore. It is a concept that has people frothing and gnashing similarly to the whole original AI debate (top down vs. bottom up, hard vs. soft). I think that some interesting discoveries and applications could come from the idea if we would quit nit-picking at its flaws, whatever they may be, and actually start trying to apply and explore the concepts.
Try reading it again - if you have to skip sections because of boredom or whatever, then do so (it is written in such a way that it doesn't need to be read 100 percent linearly). After you finish (or not, as the case may be), I could reccommend to you a whole host of other books to read on similar (and I tend to believe related) subjects (if you are interested, email me - I have also posted these lists here before on
Reason is the Path to God - Anon