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Visual Exploration of Complex Networks 90

Posted by timothy from the rock-hammer-for-scale dept.
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.'"
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Visual Exploration of Complex Networks More

Visual Exploration of Complex Networks

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  • Wow (Score:3, Funny)

    by Eightyford ( 893696 ) on Monday July 24, 2006 @06:30PM (#15772829) Homepage
    Wow, a winamp visualization.

  • Let me just say... (Score:4, Funny)

    by The Living Fractal ( 162153 ) <banantarrNO@SPAMhotmail.com> on Monday July 24, 2006 @06:31PM (#15772832) Homepage
    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

    • Let me guess... (Score:5, Funny)

      by jd ( 1658 ) <imipak@ y a hoo.com> on Monday July 24, 2006 @06:44PM (#15772886) Homepage Journal
      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.
      • Heisenberg's Writing Principle, which states that you can either know bout a topic or write about it, but not at the same time.


        Where does this leave reading about a topic?

      • Yeah, I know what you mean. Someone told me I wasn't thinking deeply enough about Mandelbrot's ideas and to take a closer look at his work, but it all just looked the same to me.

      • He's talking about Wolfram's "A New Kind of Science." I tried to read the book four times. I can't get past chapter two without flipping out. The man spends the entire book wanking about how great he is. (That he actually is great doesn't make the book any less unpleasant.) It's honestly quite frustrating - I love automata and I really want to know what is in that book.

        But, I just can't stand it.
        • If you are interested at all in this (and by the fact that you have tried to read it four times seems to indicate that you are), you should really try to "get through it".

          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 rese

          • 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.

            You are the first of several hundred people I know who've read that book to claim it was anything other than offensively self-aggrandizing, and this slashdot article shows dozens of people who see it the way that I do. At several points he claims the book is the most important in the history of science, that he's more important to mathematics than Leibniz or Euler, and even
    • I'm going to guess Stephen Wolfram's A New Kind of Science.

    • Here is it: (Score:5, Informative)

      by megaditto ( 982598 ) on Monday July 24, 2006 @07:57PM (#15773156)
      Dr. Wolfram (of Mathematica) offers PDFs of his book for free here (or pay $60 for hardcopy):

      http://www.wolframscience.com/thebook.html [wolframscience.com]

      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.

      • Re:Here is it: (Score:1, Interesting)

        by Anonymous Coward
        Yes, he got his PhD at a very young age, BUT... He seems to think that he doesn't need to adhere (anymore) to the principle of how scientific work is normally communicated to others, i.e., through peer-reviewed journal / conference publications. Instead, he puts all of his work in a book that isn't peer-reviewed by fellow scientists before being published, giving him the opportunity to make all kinds of unsubstantiated claims.

        The fact that he doesn't cite others whenever this would have been appropriate mak

        • Re:Here is it: (Score:3, Insightful)

          by NichG ( 62224 )
          Honestly, of the things that bother me about his book, that's not horribly high on the list. The main problem is, nothing he talks about has any ability to predict behavior either qualitatively or quantitatively.

          He makes no mention about how to crossover from a microscopic theory to his cellular automata stuff, so even if you can say 'wow, that looks like seashells' when you're presented with some new physical problem you can't just look up his book and figure out what the equivalent CA model would be.

          And h
        • AC, did you even really read this book?

          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,

  • 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.
  • 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?
    • People may be more complex than a neuron, but not nearly as complex as the total and their possible interactions. With billions of neurons, each interacting with 1,000 to 10,000 others the possible configurations are enormous, yet most we do not act in such a wildly differing manners. I don't know much about chaos theory, but it mentions something along the lines of the sinple behaviour of complex systems and the complex behaviour of simple systems. Thinking deeper, I am not sure which category this fa
      • Yeah, Hitler, Mother Theresa and Abe Vogoda. Identical, I tell you; no difference in their behavior at all. In other news, we're also not as complex as the sum set of possible interactions of our atoms, or of a gigantic bag of legos.

        I don't know much about chaos theory, but it mentions something along the lines of the sinple behaviour of complex systems and the complex behaviour of simple systems.

        No, it doesn't. Chaos theory discusses the predictability locusses of confluent systems and the divergent beh
        • 1) Of course they exhibit different behaviour, they are different people. The point was, for the enormous number of possible combinations of materials that comprise a person - we are not that different. We are not even the sum of our parts.

          2) Did you actually read and understand the part that says "I do not know much about ...", or did you ignore that to make your point while posturing and big noting yourself?

          3) Please don't pose.

          4) Mod me as flame/troll/off topic/whatever
    • People are obviously more complex than their neurons
      How so?
      • not to troll, but maybe he means People = Neurons + Muscles, etc. > Neurons ?

        Also, in TFA's pictures they show Petri-dish neurons (i.e. not a picture of a brain slice), seems to be cortical interneurons or something, and probably transfected with fluorescent markers. This 'network' looks cool, but is really crap compaired to brain wiring. Like comparing a silicone molecule to an 11/780.
        • Exactly, since people have neurons, they're obviously more complicated than them.

        • This 'network' looks cool, but is really crap compaired to brain wiring. Like comparing a silicone molecule to an 11/780.

          I was kind of wondering about the pictures because the article says, "A typical cortical neuron receives 1,000 to 10,000 contacts from other neurons and contacts 100 to 1,000 additional neurons." In the picture it really doesn't look like there are any more than a few connections between the neurons pictured. Do you know if it's more dense than that in reality, or did the article mean

          • 10,000 synapses/cell sounds about right in a living brain. Some cells receive many more inputs directly, e.g. a Purkinje cell in a human cerebellum can receive up to 500,000 inputs from parallel fibers. It is impossible to visualize all these inputs.

            Cultured neurons, i.e. what you get after you put a brain in a blender, then thinly spread the goo over a 2-D surface, receive about 10 to 100 inputs, if that. In this situation, with staining and labeling, one can trace all the inputs a given cell receives.

            Neur
  • 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.
  • Must agree with limited crowd, so far. This is nothing new at all. Five somewhat pretty pictures. Seen much more elsewhere.
    • Yep, they're pretty.

      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 ... and what does it mean? What information is conveyed in this manner?
    • Five somewhat pretty pictures.

      Beauty is truth, truth beauty.

      But beauty is born of complexity.

      Now do you understand why the EU Commision/Congress like simple answers to everything - and it all turns out so ugly?

      KFG
    • The best part about you saying "yawn, seen it before" is that that's the same thing people are saying about your post. The difference is that the article gave us a few mildly pretty pictures to look at.

      Next time, try adding a joke or something.
  • The article is a little short, I would have liked more more more!! :-)

    May I suggest Information Architecture from Peter Morville [wikipedia.org]. He is also co-founder and president of the Information Architecture Institute.

    May I also suggest taking a look at Prefuse [prefuse.org], an open source project to interactively vizualize organized information (still in beta however).
  • 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.

    Intriguing sure, beautiful ... huh ?

  • Tree of Strife. (Score:1, Funny)

    by Anonymous Coward
    ""Seed magazine has a story on complexity, and how it can be visually represented with fascinating results."

    [Slashdot]
    Opinions expressed as facts.
        |
        |____Disagree with...
        |
        |____Agree with...
        |
        |____Punt!
  • 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! (Score:5, Funny)

    by Speare ( 84249 ) on Monday July 24, 2006 @07:16PM (#15773019) Homepage Journal

    Wow, this is Unix! I know this!!
    --Lex

  • Large state spaces (Score:3, Interesting)

    by GileadGreene ( 539584 ) on Monday July 24, 2006 @07:19PM (#15773031) Homepage
    Something along similar lines is Frank van Ham's work on visualizing large state spaces [win.tue.nl]. He's generated some neat visualizations of complex transition systems associated with various protocols.

  • Everywhere???? (Score:2, Interesting)

    by Itninja ( 937614 )
    Complexity is everywhere
    Isn't that kind of subjective? I mean, what's simple to one person, could be incredibly complex to someone else. And as a side thought, does this mean that simplicity is no where?
    • Not necessarily arbitrary. The last I knew (and this was quite a while ago, as burgeoning physical theories go), there are four main categories into which systems are classified: Two of them are relatively "boring" from a dynamics point of view, as they correspond to systems which are pretty much locked into static configurations. One of the remaining categories is interesting, but nearly "too interesting", because the behavior there is chaotic, in a specific mathematical and dynamics sense. The remaining c
    • Complexity is everywhere

      Isn't that kind of subjective? I mean, what's simple to one person...

      Er, no. Complexity isn't the study of things that make you think hard. Complexity is the mathematical study of the uprising of effects from complex systems. Some computer scientists are familiar with a subset of this under the heading "emergent behavior." Weather is the result of complexity, as are volcanoes. Mutation is complexity. Radiation is complexity. This is one of those times at which the word is di

  • visualizing large-scale information (Score:5, Interesting)

    by Nick Mitchell ( 1011 ) on Monday July 24, 2006 @07:31PM (#15773070) Homepage
    as far as I can tell, the article only gives one quantification of the scale these folks are dealing with: on the order of tens of thousands for that one case. is this what is considered large? the point of visualizations is to show patterns of nodes (and patterns of paths) in graphy structures. at some point, one runs into one or the other of various limits:
    1. pixels on the display: 2 million or so.
    2. 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?
    3. 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.

  • Comment removed (Score:4, Informative)

    by account_deleted ( 4530225 ) on Monday July 24, 2006 @07:37PM (#15773081)
    Comment removed based on user account deletion
  • It's here : http://infosthetics.com/ [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 nu

  • The point of visualization (Score:5, Insightful)

    by espressojim ( 224775 ) <eris@NOsPam.tarogue.net> on Monday July 24, 2006 @07:54PM (#15773149)
    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.
    • I'd like to add to your post by pointing out how humans are VERY VERY good at pattern recognition. It can be attributed to many things in life...such as our basic concept of time for example. But imagine if our pattern recognition abilities were taken even further. I believe there is a scene near the end of Schizmatrix (awesome book BTW) where there is a genetically modified race of people who have enhanced pattern recognition centers of their brain, and it talks about how it affects them, particularly i
    • 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.

      I think you missed the whole point of this.
      From the TFA: "This network maps protein function by connecting proteins that share sequence similarity. Each of the 30,727 vertices represents a protein, and each of the 1,206,654 connections represents a similarity in amino acid sequence."

      So the point of th
    • From TFA: Different areas of the network tend to emphasize different functional classifications. As a result, one can infer a protein's function by the coordinate of the protein in the network. I don't do any protein research, so i can't get anything useful from seeing it this way, but I'm sure someone can.
    • 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.

      Maybe you should have read TFA more carefully, as it in fact gives two specific cases of utility.

      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.

      Do not confuse your infamiliarity wi
      • I think putting up graphs without even the kindness of a legend, labels, etc is not terribly useful. And while I'm not in the protein folding field, I'm close by in genetics, and we look at protein folding and how mutations affect active sites.

        You need to re-read this bit:

        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.

        Like I said in another post, these guys ought to read a bit of Tufte, so th
        • Thanks for letting me know what I don't do for a living, though, as I'd nearly forgotten and thought I was a professional chef.

          Huhuhhuuhu. Well said, sir. I believe I'll be stealing that.

          I think putting up graphs without even the kindness of a legend, labels, etc is not terribly useful.

          Uh huh. That's kind of the issue here, though, is that this isn't meant to be useful. It's pretty, it's a tease, and they're trying to get you to buy the magazine, which has exactly what you're suggesting should be there.
          • You need to re-read this bit: Maybe you should have read TFA more carefully, as it in fact gives two specific cases of utility.

            They *tell* you it's useful, but they don't supply the information to demonstrate that it's useful.

            I can tell you about this great bridge I have to sell you. Can you tell me why you'd want to listen to me and give me money?

            • You are pointlessly combattive in your desperation to ignore that they're not giving you data for free in the effort to sell you something. I understood the data they gave. If you don't, that's your problem. And no, I don't fall for sold bridges; that's why I don't own the magazine.

              Enough with the personal attacks. I think I won't be speaking with you again.
  • This makes me think about the character Gentry from William Gibsons Mona Lisa Overdrive who was looking for the shape of the matrix.
  • For some more information on network visualization check out David Glecih's website http://www.stanford.edu/~dgleich/ [stanford.edu]. 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 [stanford.edu]

  • A picture speaks louder... (Score:4, Interesting)

    by Cicero382 ( 913621 ) <clancyj@tiscalBLUEi.co.uk minus berry> on Tuesday July 25, 2006 @08:18AM (#15775206)
    ...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.
  • 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 S

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