BC Net Workshop: Trends and perspectives in complex networks

December 10-12, 2008 at the Physics Department, University of Barcelona, Barcelona, Spain.

Organizers: Marian Boguñà (U. Barcelona), Albert Díaz-Guilera (U. Barcelona) Romualdo Pastor-Satorras (U. Politècnica de Catalunya), and M. Àngels Serrano (IFISC).

Description: Ten years have elapsed since the publication of the celebrated paper by Watts and Strogatz on small-world networks. During this decade, the development of foundational aspects and methodologies set the grounds of complex network science, an interdisciplinary research area connecting Statistical Physics, Biology, Information Technology, Sociology, Economy, and others. Time has come to ask what have been the major contributions of this emerging field to prospect its future in perspective. We believe network science is now mature enough to start developing problem-solving ability and engineering and predictive power.

The spirit of this workshop is to stimulate researchers in complex networks and related areas to find new perspectives, trends, and applications that guarantee this headway. To this end, internationally recognized specialists will be invited to explain their current investigations and to discuss the expected progress of their research within the context of the field. The workshop will present as well selected contributions compliant with its purpose. An open colloquium session will also be organized where keynote speakers, participants, and committee members will have the opportunity to debate all together on the present situation of complex networks science and its outlook.

It's hard to top the explanation that NASA's Astro Photo of the Day page gave to this video, so I'll just quote it here:

What are these humans doing? Dancing. Many humans on Earth exhibit periods of happiness, and one method of displaying happiness is dancing. Happiness and dancing transcend political boundaries and occur in practically every human society. Above, Matt Harding traveled through many nations on Earth, started dancing, and filmed the result. The video is perhaps a dramatic example that humans from all over planet Earth feel a common bond as part of a single species. Happiness is frequently contagious -- few people are able to watch the above video without smiling.

And yet despite these vast differences in the basic physical world that different types of species encounter, the distribution of body sizes within a taxonomic group, that is, the relative number of small, medium and large species, seems basically the same regardless of whether we're talking about insects, fish, birds or mammals: a few species in a given group are very small (about 2 grams for mammals), most species are slightly larger (between 20 and 80 grams for mammals), but some species are much (much!) larger (like elephants, which weigh over 1,000,000 times more than the smallest mammal). The ubiquity of this distribution has intrigued biologists since they first began to assemble large data sets in the second-half of the 20th century.

Many ideas have been suggested about what might cause this particular, highly asymmetric distribution, and they basically group into two kinds of theories: optimal body-size and diffusion. My interest in answering this question began last summer, partly as a result of some conversations with Alison Boyer in another context. Happily, the results of this project were published in Science last week [3] and basically show that the diffusion explanation is, when fossil data is taken in account, really quite good. (I won't go into the optimal body-size theories here; suffice to say that it's not as popular a theory as the diffusion explanation.) At its most basic, the paper shows that, while there are many factors that influence whether a species gets bigger or smaller as it evolves over long periods of time, their combined influence can be modeled as a simple random walk [4]. For mammals, the diffusion process is, surprisingly I think, not completely agnostic about the current size of a species. That is, although a species experiences many different pressures to get bigger or smaller, the combined pressure typically favors getting a little bigger (but not always). The result of this slight bias toward larger sizes is that descendent species are, on average, 4% larger than their ancestors.

But, the diffusion itself is not completely free [5], and its limitations turn out to be what cause the relative frequencies of large and small species to be so asymmetric. On the low end of the scale, there are unique problems that small species face that make it hard to be small. For instance, in 1948, O. P. Pearson published a one-page paper in Science reporting work where he, basically, stuck a bunch of small mammals in an incubator and measured their oxygen (O2) consumption. What he discovered is that O2 consumption (a proxy for metabolic rate) goes through the roof near 2 grams, suggesting that (adult) mammals smaller than this size might not be able to find enough high-energy food to survive, and that, effectively, 2 grams is the lower limit on mammalian size [6]. On the upper end, there is an increasingly dire long-term risk of become extinct the bigger a species is. Empirical evidence, both from modern species experiencing stress (mainly from human-related sources) as well as fossil data, suggests that extinction seems to kill off larger species more quickly than smaller species, with the net result being that it's hard to be big, too.

Together, this hard lower-limit and soft upper-limit on the diffusion of species sizes shape distribution of species in an asymmetric way and create the distribution of species sizes we see today [7]. To test this hypothesis in a strong way, we first estimated the details of the diffusion model (such as the location of the lower limit and the strength of the diffusion process) from fossil data on about 1100 extinct mammals from North America that ranged from 100 million years ago to about 50,000 years ago. We then simulated about 60 million years of mammalian evolution (since dinosaurs died out), and discovered that the model produced almost exactly the size distribution of currently living mammals. Also, when we removed any piece of the model, the agreement with the data became significantly worse, suggesting that we really do need all three pieces: the lower limit, the size-dependent extinction risk, and the diffusion process. The only thing that wasn't necessary was, surprisingly, the bias toward slightly larger species in the diffusion itself [8], which I think most people thought was necessary to produce really big species like elephants.

Although this paper answers several questions about why the distribution of species body size is the way it is, there are several questions left unanswered, which I might try to work on a little in the future. In general, one exciting thing is that this model offers some possibilities for connecting macroevolutionary patterns, such as the distribution of species body sizes over evolutionary time, with ecological processes, such as the ones that make larger species become extinct more quickly than small species, in a relatively compact way. That gives me some comfort, since I'm sympathetic to the idea that there are reasons we see such distinct patterns in the aggregate behavior of biology, and that it's possible to understand something about them without having to understand the specific details of every species and every environment.

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[1] An organism's size is closely related, but not exactly the same as its mass. For mammals, their density is very close to that of water, but plants and insects, for instance, can be less or more dense than water, depending on the extent of specialized structures.

[2] The typical mammal species weights about 40 grams, which is the size of the Pacific rat. The smallest known mammal species are the Etruscan shrew and the bumblebee bat, both of whom weight about 2 grams. Surprisingly, there are several insect species that are larger, such as the titan beetle which is known to weigh roughly 35 grams as an adult. Amazingly, there are some other species that are larger still. Some evidence suggests that it is the oxygen concentration in the atmosphere that mainly limits the maximum size of insects. So, about 300 million years ago, when the atmospheric oxygen concentrations were much higher, it should be no surprise that the largest insects were also much larger.

[3] A. Clauset and D. H. Erwin, "The evolution and distribution of species body size." Science 321, 399 - 401 (2008).

[4] Actually, in the case of body size variation, the random walk is multiplicative meaning that changes to species size are more like the way your bank balance changes, in which size increases or decreases by some percentage, and less like the way a drunkard wanders, in which size changes by increasing or decreasing by roughly constant amounts (e.g., the length of the drunkard's stride).

[5] If it were a completely free process, with no limits on the upper or lower ends, then the distribution would be a lot more symmetric than it is, with just as many tiny species as enormous species. For instance, with mammals, an elephant weights about 10 million grams, and there are a couple of species in this range of size. A completely free process would thus also generate a species that weighed about 0.000001 grams. So, the fact that the real distribution is asymmetric implies that some constraints much exist.

[6] The point about adult size is actually an important one, because all mammals (indeed, all species) begin life much smaller. My understanding is that we don't really understand very well the differences between adult and juvenile metabolism, how juveniles get away with having a much higher metabolism than their adult counterparts, or what really changes metabolically as a juvenile becomes an adult. If we did, then I suspect we would have a better theoretical explanation for why adult metabolic rate seems to diverge at the lower end of the size spectrum.

[7] Actually, we see fewer large species today than we might have 10,000 - 50,000 years ago, because an increasing number of them have died out. The most recent population collapses are certainly due to human activities such as hunting, habitat destruction, pollution, etc., but even 10,000 years ago, there's some evidence that the disappearnace of the largest species was due to human activities. To control for this anthropic influence, we actually used data on mammal species from about 50,000 years ago as our proxy for the "natural" state.

[8] This bias is what's more popularly known as Cope's rule, the modern reformulation of Edward Drinker Cope's suggesting that species tend to get bigger over evolutionary time.

IEEE Conference on Data Mining (ICDM) Workshop on Analysis of Dynamic Networks (ADN)

December 19, 2008 at the IEEE Conference on Data Mining (ICDM) in Pisa, Italy

Organizers: Tanya Berger-Wolf (UIC), Malik Magdon-Ismail (RPI) and Jared Saia (UNM).

Description: The goal of the Analysis of Dynamic Networks (ADN) workshop is to bring together research that addresses explicitly the dynamic nature of networks in the context of analysis of social, electronic, biological and other networks. We aim to further the development of a computational framework in which one can model, discover and analyze complex interaction systems as they form and evolve.

We invite contributions presenting new computational methods for analysis of dynamic interaction networks, new models of dynamic behavior of networks, or applications of dynamic network analysis in various contexts. Papers presenting new methods should provide experimental or empirical evidence of the performance of the new methods.

In this context, submission topics can include, but are not limited to:
- Modeling dynamic behavior of networks

- Network structure prediction

- Analysis of spreading processes in networks

- Community structure inference

- Search and routing in dynamic networks

- Identification of critical nodes

- Comparison of dynamic networks

- Visualization of dynamic networks

Other topics within the subject area are welcome. Note, that all submitted papers should demonstrate the relevance to the topic of dynamic networks. If unsure whether your paper fits the session theme, please contact one of the co-chairs.

Update 18 July 2008: Having just received an invitation to speak from the organizers, I think it's likely that I'll be attending. In addition to learning about new science at DIMACS, it'll be a great opportunity to also visit some friends and colleagues in New York City.

DIMACS / DyDAn Workshop on Network Models of Biological and Social Contagion

November 3 - 4, 2008 at DIMACS, Rutgers

Organizers: Lauren Ancel Meyers (UT Austin) and Michelle Girvan (UMD).

Description: The spread of infectious diseases and the flow of ideas and information through populations fundamentally depend on the complex structure of the underlying network of interactions between individuals. Disease ecologists and sociologists have historically studied the dynamics of contagion using models that assume very simple population structures. Recently, however, network modeling has revolutionized both fields by enabling the rigorous exploration of the relationship between complex individual-level behavior and the higher-level emergence of outbreaks. The field draws on advanced statistical tools for inferring network structure from often limited data, data-driven algorithms for generating realistic network structures, and mathematical approximations for predicting transmission dynamics that draw from the methods of percolation theory and other fields within statistical physics.

While network models are more complex than their mass-action predecessors, they are remarkably tractable, often reducing to low-dimensional descriptions and allowing straightforward calculations of the dynamics of contagion. The fields of infectious disease epidemiology and sociology are simultaneously experiencing an explosion of computationally-intensive agent-based simulation models, that allow much higher-resolution representations of populations but often preclude comprehensive analysis. Selecting among the diversity of modeling approaches is non-trivial, and may be highly dependent on the system and the questions.

This workshop will focus on network models for biological and social contagion, and how they compare to alternative approaches. It will address the challenges of inferring network structure from sociological and/or epidemiological data, understanding the emergence of such network structure from simple individual-level behavior, and predicting the dynamics of contagion from simple characterizations of the underlying network.

Topics:

- Inferring network structure from data

- Generative models of social and epidemiological networks

- Modeling the dynamics of biological and social contagion on networks

- Modeling feedback from contagion dynamics to network structure

- Model selection -- choosing the right level of complexity

Update 18 July 2008: My lecture notes are now online on the Beijing CSSS wiki here. I gave two lectures on the basics of complex networks, and one lecture on power laws in empirical data.

Update 18 July 2008: It's hard to summarize the overall impression I had of Beijing in particular, and China in general; but, I'll try. Beijing is a city bustling with life. A wide zone of feverish development and heavy air pollution (but not trash - the city was surprisingly clean except for the dingy-ness the heavy smog left on all surfaces), it's also a study of different aspects of Chinese society modernizing at different rates. Beijing is knocking down the traditional hutongs (the traditional, one or two story residential and light commercial buildings that used to blanket the Beijing landscape), often over the protests of their residents but not always, to build the skyscrapers and apartment towers of a modern, dense city. The exchange rate, and government policies, made taxis very affordable, and while riding around, I spotted both the ugly concrete towers as well as the beautiful glass and steel constructions that would look at home in Zurich or New York. There were also several buildings most notable for their striking architecture [1]; most of these are for the Olympics, but not all of them - some were simply upscale residential, office or hotel buildings.

It wasn't clear to me that the regular Beijinger was excited about the upcoming Olympics (starting in just three weeks), but certainly the government is. Olympic decorations and advertisements were everywhere, and the mascots (the five Fuwa) were ubiquitous. And yet, when I walked through the Olympic pavilion area, there was obviously still a tremendous amount of work left to be done. I'm told that Athens was even further behind schedule for the 2000 Olympics, so maybe it will all work out. The Olympic areas were also some of the places where the military presence was the strongest, with lots of fences and guards. Oddly, there was even a military installation (with tanks) just to the south of one of the sports complexes.

My favorite picture of the 300 odd that I took (many of which are now on my Flickr photostream) while bouncing around the city is of a man on a traditional bicycle yakking away on his cell phone. A lot of people still ride bicycles, but apparently cars are increasingly popular. Owning one is now a status symbol, as it used to be in America [2], and many Beijingers are taking to it with enthusiasm, even though the traffic is already terrible. I'm told that some American car makers are doing very well in the growing Chinese market, to the point that their recent growth was driven almost entirely by Chinese sales [3]. Designer goods that are fashionable in the West are also popular in Beijing, but surprisingly, they don't cost any less. So, a well-to-do Beijinger will spend $300 on a Coach purse even though it costs 2000 yuan, enough to buy a nice dinner every night for a month. Another interesting observation about Beijing is that most of the commercial stores (not the small businesses, but rather the larger enterprises) were overstaffed. At several restaurants, I noticed at least three or four times as many waitstaff as were necessary to actually run the place. I'd like to think this is indicative of the larger problem China faces with a burgeoning labor force, but who knows.

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[1] Coincidentally, the NY Times put up an interactive graphic that discusses five of these, mostly built in prep for the Olympics. I saw all of them, from a distance, except for "Big Shorts" (the new national television building). The New Yorker also has a short piece about these buildings, and the Beijing skyline in general. I highly recommend both of these.

[2] Owning a car in the US is no longer enough to show everyone else that you're rich and know it. Now you have to drive a big car, preferably something like a Hummer or an FJ.

[3] Someone told me that Buick, of all brands, is very popular in China because it was the brand that the last Emperor favored.

A recent study in the Proceedings of the National Academy of Science (USA) adds another trait: sleeping [1,2], at least among song birds. By hooking up some zebra finches to the machinery usually used to measure the brain activity of sleeping mammals, Philip Low and his colleagues discovered that song-bird brains exhibit the same kind of sleeping-brain activity (slow waves, REM, etc.) normally seen in mammals. The authors avoid the simplistic explanation that the cause of this similarity is due to a shared ancestry, i.e., mammalian-style sleep evolved in the common ancestor of birds and mammals, which would be about 340 million years ago (with the origin of the Amniote class of animals). This hypothesis would imply (1) that all birds should sleep this way (but the current evidence suggests that it's only song-birds that do so), and (2) that other amniotes like lizards would have mammalian-like sleep patterns (which they apparently do not).

So, the similarity must therefore be an example of convergent evolution, i.e., birds and mammals evolved this kind of sleep behavior independently. The authors suggest that this convergence is because there are functionally equivalent regions of mammal and bird brains (a familiar idea for long-time readers of this blog) [3] and that these necessitate the same kind of sleep behavior. That is, song birds and mammals sleep the same way for the same reason. But, without understanding what mammalian-like sleep behavior is actually for, this could be mere speculation, even though it seems like it's on the right track. Given the other similarities of complex behavior seen in birds and mammals, it's possible that this kind of sleep behavior is fundamental to complex learning behaviors, although there could be other explanations too (e.g., see [3] below). At the very least, this similarity of behavior in evolutionarily very distant species gives us a new handle into understanding why we, and other species, sleep the way we do.

Update 30 June 2008: The New York Times also has an article in its science section about this phenomenon.

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[1] "Mammalian-like features of sleep structure in zebra finches." P. S. Low, S. S. Shank, T. J. Sejnowski and D. Margoliash. PNAS 105, 9081-9086 (2008).

A suite of complex electroencephalographic patterns of sleep occurs in mammals. In sleeping zebra finches, we observed slow wave sleep (SWS), rapid eye movement (REM) sleep, an intermediate sleep (IS) stage commonly occurring in, but not limited to, transitions between other stages, and high amplitude transients reminiscent of K-complexes. SWS density decreased whereas REM density increased throughout the night, with late-night characterized by substantially more REM than SWS, and relatively long bouts of REM. Birds share many features of sleep in common with mammals, but this collective suite of characteristics had not been known in any one species outside of mammals. We hypothesize that shared, ancestral characteristics of sleep in amniotes evolved under selective pressures common to songbirds and mammals, resulting in convergent characteristics of sleep.

[2] New Scientist has a popular science piece about the PNAS article.

[3] Mammals and birds have another important convergent similarity: they are both warm-blooded, but their common ancestor was cold-blooded. Thus, warm-bloodedness had to evolve independently for birds and for mammals, a phenomenon known as polyphyly. One interesting hypothesis is that warm-bloodedness and mammalian-like sleep patterns are linked somehow; if so, then presumably sleeping has something fundamental to do with metabolism, rather than learning as is more popularly thought. Of course, the fact that the similarity in sleeping seems to be constrained to song-birds rather than all birds poses some problems for the metabolism idea.

I'll be giving a talk (1:30pm, Building 42, 2nd Floor; not sure if it's open to the public) about complex models of large-scale structure in networks at Google's Mountain View complex tomorrow, as part of a joint SFI workshop entitled "Selection Tinkering and Emergence in Complex Networks." The workshop is part of the Paramaribo Tech Talk series at Google; here's a brief explanation of the event:

This meeting will search for general principles of organization and evolution of natural and artificial systems changing through local rules based on reuse of previously existing substructures. Such a process of "tinkering" makes a big difference (at least in principle) when comparing biological structures and man-made artifacts. As pointed out by the French biologist François Jacob, the engineer is able to foresee the future use of the artifact (i.e. it acts as a designer) whereas evolution does not. The first can ignore previous designs, whereas the second is based on changes taking place by using available structures.

In spite of its apparent drawbacks, tinkering has been able to generate most complex structures observable in the real world (including some in the technological world). Very often, the resulting structures share common principles of organization, suggesting that convergent evolution towards a limited number of basic plans is inevitable. How innovations emerge through evolution is one of the key problems in complexity, and this meeting will focus towards understanding these problems, using several scales of analysis - from cellular networks and tissues to ecosystems - and using network approaches as a quantitative characterization of such complexity.

My contribution, I believe, is to talk about networks and how to extract meaningful information about their large-scale structure.

Update 28 June 2008: The visit to Google went quite well, I think. The Tech Talk was in one of the main buildings, and what seemed like a relatively central place. Throughout the day, Googlers passed by on their way to other places in the complex. During my talk, I noticed a few new faces in the audience, which I can only assume were locals.

What's fascinating about Google is, really its size. My understanding is that the core business -- the one that brings in the majority of the money -- is the AdSense division, which sells keywords to advertisers and places ads on various other sites. The AdSense group itself doesn't require much to run, so there's a tremendous surplus of cash, which Google has apparently been using to grow like crazy and to invest in interesting (but mostly not profitable) projects related to organizing information. In some sense, this makes Google a lot like the old Bell Labs, where massive amounts of extra money were devoted to risky projects, many of which didn't produce anything useful until years or decades later. On the other hand, there's a lot to be said for having a good reputation, and the kind of good PR that Google gets from free but useful products like GoogleEarth, etc. is the kind that you simply can't buy any other way.

Another thing that struck me about the Googleplex was the age demographic. One of my friends from grad school who works there now said that a quarter of everyone he meets has worked there for less time than he has. That's not because there's a high turnover rate, but because Google's just been hiring like crazy. And they've been hiring young people. The vast majority of people I saw were under 40 or so, and a big portion of them were under 30.

So, it's a strange place really -- not like most companies I've interacted with --lots of fringe benefits (free food everywhere, free services like haircuts and shuttles, 20% time to work on your own crazy projects, etc.), lots of freedom, lots of young people, etc. In some sense, the internal corporate philosophy seems to be one of bringing together lots of smart people and giving them the tools, impetus and freedom to do brilliant things. So, it seems like a great place to work, right now. If the cash surplus situation were to change dramatically for some reason (government anti-trust activity a la Microsoft, strong competition from Yahoo! or MSN, a collapse of Internet adversing, etc.), then I'm sure things would change, much as they did for Bell Labs in the 1990s when it was spun out from AT&T.

For researchers, Google seems like a pretty good place to be. The three Googler colleagues of mine that I chatted with while I was there all have PhDs and all seemed to be really happy with their jobs. Of course, none had been there for that long, but one of them, who works on understanding the internal organizational dynamics of the company, mentioned that the retirement / quitting rate is very very low. So, like I said, it seems like a really good place to work, for now.

It's hard, of course, to really get your head around how big a problem the energy-question is. We all know by now that we should use less oil, that we should buy more fuel efficient cars, that we should have better insulated houses, lower-power refrigerators, etc.; there are lots of shoulds floating around in the media. And then there are the sky-is-falling types, who say that if we don't do all these things immediately, then the planet is going to overheat, the oceans will rise 100 feet, and civilization will be cast 4000 years back to the Stone Age. Fear can be a powerful motivator, but only when it's clear what the right reaction is. Unfortunately, for an average person who wants to have a positive impact, to do their part in saving the world, it's not at all clear what can be done, or even how much urgency is really warranted.

Last week, Prof. Nathan Lewis (CalTech) visited SFI as our colloquium speaker. Lewis has been trying to get his head around just how big the problem of sustainable growth is, and then translate it into understandable terms. I wasn't that thrilled with the style of his presentation, but the content itself was great and the message was rather clear.

First, there's the question of what are the consequences of climate change. If the consequences are small, then maybe it's okay to ignore the whole problem. Unfortunately, the last time we know for a fact that carbon dioxide (CO2) levels were close to what they are approaching now, 90% of all life on the Earth became extinct. This catastrophe happened about 251.4 million years ago (for comparison, dinosaurs died out about 65.5 million years ago), and is called the end-Permian extinction event. To put it in more clear terms how big an extinction this was, it's the only time in all of Earth's history that cockroaches almost became extinct. This is not, of course, to say that 90% of all life on Earth (possibly including us) will become extinct over the next few centuries or millennia because of the increased (and increasing!) CO2 levels we're experiencing, but that we have very little experience with or expectation about what happens when CO2 levels are this high, and the only data point we do have (the end-Permian event) suggests that things could be very bad. So, it might be useful for us to try to avoid venturing into such unknown territory. We only have one planet to experiment with, after all.

So, if we're resolved to avoid end-Permian-like CO2 levels, what can we do? If you think that human-generated CO2 makes no significant contribution to the global CO2 levels, then you don't have many options that don't involve actively extracting CO2 from the atmosphere (e.g., planting lots and lots of trees). On the other hand, if you, like the vast (vast!) majority of climate scientists, think that human-generated CO2 is the main culprit of rising CO2 concentrations (and temperatures), then we have lots of options, since we theoretically have control over how much CO2 we as humans emit [2]. Unfortunately, one of Lewis's points is that, given the scale of the problem we face and how much time we have left to solve it, simply reducing CO2 output is not going to be enough. That is, being green enough to save the planet as we know it is going to require a major reallocation of our civilization's resources; business-as-usual, or even a half-assed attempt, is not going to make a big enough change in atmospheric CO2 concentrations to prevent the planet from being irrevocably changed (heated) for the next 3000 years or more.

To keep CO2 levels from approaching end-Permian levels, we basically have to eliminate almost all CO2 emissions from human industrial activities, everywhere on Earth, within the next 50 years. That's a huge task, especially considering that China recently became the world's largest emitter of CO2, and, along with the US, shows little interest in reducing its emissions. (Scare-tactics go both ways, and the usual argument against doing anything is that it will hurt hurt economic growth, cost jobs, etc. This is ridiculous, of course, since there are huge economic gains to be won by being successful at creating clean, abundant energy.)

Fortunately, there's a good solution at hand: solar energy. Unlike other sources (wind power, tidal power, geothermal power, biofuels, etc.), solar energy is incredibly abundant (1000 times more abundant than wind power), and could satisfy the energy demands of the entire planet using today's technology. Some estimates say that enough sunlight falls on the southeast quarter of New Mexico to power the entire United States. In fact, solar energy is so abundant that covering only something like 1% of the Earth's land with solar panels would give us plentiful power in perpetuity. And, as a bonus, solar power emits basically no carbon dioxide.

The hurdles to a solar-powered future are twofold (there are others, too, but these are the big ones). First, there are the political problem with getting all of civilization to embrace this solution now, rather than in 50 years when it's too late (that is, in 50 years, if we've done nothing significant, CO2 levels will already be at their end-Permian levels). The political climate does seem to be changing a little, but the inertia in the direction of ignoring the problem and burning our way back to the end-Permian is very very strong. The second problem is that energy from the sun is still a lot more expensive than energy from oil and coal, so there's not yet an economic incentive to get behind solar power. For the average citizen, then, there's not much to do that won't cost (possibly a lot) more money, and this severely limits the ability of the populace to use their economic leverage to drive the switch to solar power. This last part is where carbon taxes or a cap-and-trade system can change the balance, by making oil and coal more expensive relative to solar. If these systems can be put in place relatively soon, and the political climate continues to become more favorable to large-scale changes to where we get our energy and how we use it, we may be able to avoid end-Permian-level CO2 concentrations. Plus, if we solve the energy problem (and with it the CO2 problem), there are other important problems (e.g., water, food, etc.) that we will, in principle, also be able to solve. It's a bright future, if only we can find it in ourselves to collectively get there.

Update 27 May 2008: In the comments, "diarmuid" points out that David MacKay, a well known expert on learning algorithms, inference and information theory, comes to basically the same conclusions above about how to solve the energy-climate problem. MacKay has even written a book "Without Hot Air" about it, for those interested in more. (It looks like a draft of the book is available for free download.)

Update 29 May 2009: Bela Nagy tells me that there's another summer school on climate change, with the impressive-sounding name The International Graduate Summer School on Statistics and Climate Modeling. This one is being run at CU-Boulder by the National Center for Atmospheric Research (NCAR) and the Institute for Mathematics Applied to Geosciences (IMAGe). It runs August 9-13, and they'll be accepting applications up until June 15th. The organizers are Stephan Sain (NCAR), Doug Nychka (IMAGe, NCAR), Claudia Tebaldi (NCAR), Caspar Ammann (NCAR), and Bo Li (NCAR and Pudue).

Update 14 June 2008: Carl Zimmer, science writer and author of a number of best-selling popular science books, now also has an essay on the end-Permian extinction and its relationship to the current warming trend, which says much the same thing about the threat life on Earth faces from increased CO2 levels.

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[1] Climate Change Summer School July 14th - August 1st, 2008

Organized By: Chris Jones (UNC Chapel Hill), Inez Fung (U.C. Berkeley), Eric Kostelich (Arizona State University), K.K. Tung (U. Washington), and Mary Lou Zeeman (Bowdoin College).

[2] A nice paraphrasing of what the industrial revolution has done to the atmosphere is this: burning coal and oil in our factories and cars has had a similar effect on the atmosphere as if a massive volcano had been erupting continuously, with ever increasing ferocity, for 200 years or so.

Anyway, there's a lot more to say on this topic, but I'll wait until the paper comes out to say it. In general, it's been a lot of fun learning about evolution and ecology, and I hope to do some more work in this area in the future.

GA Tech's Frontiers in Multi-Scale Systems Biology

October 18-21, 2008 at Georgian Terrace Hotel, Atlanta, GA

Organizers: Jeffrey Skolnick (Co-Chair), Eberhard Voit (Co-Chair), David Bader, Lynn Durham, Richard Fujimoto, Jessica Gilmore, Melissa Kemp, Patricia Sobecky, LaDawn Terry, Eric Vigoda.

Description: Frontiers in Multi-Scale Systems Biology will highlight representative topics of multi-scale systems biology including: genomics, proteomics, metabolomics, molecular inventories and databases, modeling and simulation, high-performance computing, enabling experimental and computational technologies, and applications in cancer, neuroscience and the environment.

Conference themes are
1. The creation of key molecular inventories that drive integrative biological systems analyses at all significant levels of biological organization.
2. Enabling experimental technologies for the investigation of multi-level, multi-scale integrative biological systems.
3. Innovation in high-performance computing, modeling and simulation, with applications in multi-scale integrative biology.
4. Applications of enabling experimental and computational technologies and molecular inventories.

There's been a lot of interest among both physicists and biologists in methods for extracting either modular or hierarchical structure in networks. In fact, one of my first papers in grad school was a fast algorithm for clustering nodes in very large networks. Many of the methods for getting at the hierarchical structure of networks are rather ad hoc, with the hierarchy produced being largely a byproduct of the particular behavior of the algorithm, rather than something inherent to the network itself. What was missing was a direct model of hierarchy.

Many of you will know (perhaps from here or here), that I've done work in this area with Cris Moore and Mark Newman, and that I care a lot about null models and making appropriate inferences from data. Our first paper on hierarchy is on the arxiv; in it, we showed some fancy things you could do with a model of hierarchy, such as assign connections a "surprisingness" value based on how unlikely they were under our model. Our second paper, in which we show that hierarchy is a very good predictor of missing connections in networks appeared today in Nature. [2,3] There's also a very nice accompanying News & Views piece by Sid Redner. Accurately predicting missing connections has many applications, including the obvious one for homeland security, but also for laboratory or field scientists who construct networks laboriously, testing or looking for one or a few edges at a time.

Another nice thing that came out of this work is that the hierarchy we extract from real networks seems to be extremely good at simultaneously reproducing many other commonly measured statistical features of networks, including things like a right-skewed degree distribution, high (or low) clustering coefficients, etc. In some sense, this suggests that hierarchy may be a fundamental principle of organization for these networks. That is, it may turn out that different kinds of hierarchies of modules is partly what causes real-world networks to look the way they do. General principles like this are wonderful (but not easy) to find, as they suggest we're on the right track to boiling a complex system down to its fundamental parts.

Of course, there are several important missing pieces from this picture, one of which is that real networks are often functional, while the hierarchical model may not completely circumscribe the networks that accomplish the necessary functions for the biological or social context they exist in. In that sense, we still have a long way to go before we understand why things like genetic regulatory networks are shaped the way they are, but hierarchy at least gives us a reasonable way to think about the large-scale organization of these fantastically complex systems.

Update 5 May 2008: Coverage of our results have appeared on Roland Piquepaille's Technology Trends, and also on Slashdot. Now I can live my days out in peace knowing that something I did made it on /. ...

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[1] Hierarchical group structure is different from a hierarchy on the nodes themselves, which is more like a military hierarchy or an org-chart, where control or information flows from individuals higher in the hierarchy to other individuals lower in the hierarchy. For gene networks, there is probably some of both kinds of hierarchy, as there are certainly genes that control the behavior of large numbers of other genes. For instance, see

G. Halder, P. Callaerts and W.J Gehring. "Induction of ectopic eyes by targeted expression of the eyeless gene in Drosophila". Science 267, 1788–1792 (1995).

[2] "Hierarchical structure and the prediction of missing links in networks." A. Clauset, C. Moore and M. E. J. Newman. Nature 453, 98 - 101 (2008).

The code for fitting the model to network data (C++), for predicting missing connections in networks (C++), and for visualizing the inferred hierarchical structure (Matlab) is available on my website.

[3] It's especially nice to have this paper in print now as it was the last remaining unpublished chapter of my dissertation. Time for new projects!

Andrew Gelman (Statistics and Political Science at Columbia U.) has a nice critique about the trouble with social sciences that he's put under the pithy heading of "Thou shalt not sit with statisticians nor commit a social science". I admit that I'm deeply sympathetic to these criticisms, at least partially because in spite of a lot of effort, and a lot of writing, the social sciences don't appear to have produced much. Of course, there are lots of plausible explanations for this, including the usual refrain that social sciences are much harder than the natural sciences because humans are wily creatures, culture changes over time but has a huge influence on human behavior, and even 10^9 humans is nothing compared to the 10^20s of particles statistical physicists often consider. Another explanation that was mentioned at my workshop by Carter Butts is that relative to the natural sciences, the social sciences are drastically under-funded and under-staffed. One of my personal suspicions, however, is that social science has been hindered by a lack of good data by which to actually test the theories social scientists kick around. This kind of empirical vacuum can encourage researchers to develop all sorts of bad habits, and physicists interesting in social science topics (e.g., opinion dynamics) are by no means immunized against these by nature of the physics training.

This summer, Dirk Helbing and colleagues are running a workshop on the future of quantitative sociology; held in Zurich August 18-23, which looks quite interesting. (Frank Schweitzer is another of the organizers, and on the first night of my workshop, Frank told me about a similar meeting on sociophysics that he helped organize back in 2002.) Dirk is an exception among physicists working on sociological questions, as he actually conducts controlled experiments on human traffic behavior in his laboratory. These have produced some very nice results, and developed some nice connections with turbulent flows. But, there are a host of other sociological questions that have, for the most part, remained wholly inaccessible to controlled experimentation. Matt Salganik's presentation about his experimental work using an online environment got me very excited about the possibility that computer technology can help solve some of the tricky problems with social influence, framing effects, etc. that usually make experiments in this area inconclusive. Another interesting possibility is behavioral economics (which ETH Zurich is strong in). That is, perhaps by adapting techniques from these experiments, we can better understand, for instance, the roles that imitation and homophily play in the way humans modify their behavior in social settings.

Naturally, the interest in controlled experiments or in physics-style modeling of social phenomena is not new, and sociologists have been arguing over how best to study social behavior for more than 100 years. The recent interest by physicists in social phenomena may, in part, be explainable by the massive amounts of electronically collected data now available. Sociologists seem to have noticed too, to some degree. For instance, a lengthy article by Emirbayer from 1997 in the American Journal of Sociology criticizes sociology's tendency to focus on static or inherent properties of people rather than on the dynamic or process-based emphasis that appeals more to physicists. At the workshop, John M. Roberts gave a nice presentation of the historical interactions between physics and sociology, but pointed out that usually sociolgists' interest in dynamic or process-based models didn't last more than a few years each time it cropped up, possibly because sociologists often relied on metaphorical models (e.g., thinking of the social equivalents of "heat" or "leverage") that ultimately didn't help them make any real predictions. From my point of view, if this revival of interest in dynamic and quantitative models of social behavior is to turn into real scientific progress, then I think the key is going to be better testing of models with data. It's easy (and fun!) to do math, but it's not science until there's a meaningful comparison with real data.