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1. Complex Systems in the Social & Physical Sciences [By Bestiario]
2. Science Magazine: Complex Systems & Networks [Repost from July 27]
3. Classic Model from Complex Systems: The El Farol Bar Problem

As a member of the Society for Evolutionary Analysis in Law (SEAL), I have had the oppurtunity to see a number of interesting presentations by Gregory Todd Jones. Gregory is a Faculty Research Fellow and Adjunct Professor of Law at the Georgia State University College of Law as well as Senior Director of Research and Principal Scientist at the Network for Collaborative Problem Solving. Of particular interest to readers of this blog, he is also the founding director of the Computational Laboratory for Complex Adaptive Systems at Georgia State Law School.

Above is a recent talk by Gregory at the TedX Atlanta in which he (1) assembles a model of sustainability based on collaboration and (2) discusses species behavior … from slugs to chimpanzees.  If you are interested in learning more … Gregory has launched a really cool blog … Cooperation Science Blog … Check it out!

Related posts:

1. Jones & Brosnan: Local Interaction, Strategy Dynamics and the Emergence of Reciprocity in Chimpanzee Social Networks
2. Science Magazine: Complex Systems & Networks [Repost from July 27]
3. SEAL 2009 @ Vanderbilt Law School

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3. Netflix Challenge for SCOTUS Prediction?

It is the final hours over at the Intrade’s Oscar Prediction Market.  While many of the categories are runaways, the race for Best Picture is tightening.  Will Avatar (click here for chart) or The Hurt Locker (click here or above for chart) prevail?  Watch tonight to find out ….

Related posts:

1. Electricity Market Simulations @ Argonne National Labs
2. Netflix Challenge for SCOTUS Prediction?

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Last summer, Dan, Jon and I wrote our conference paper for ASNA 2009 entitled On the Stability of Community Detection Algorithms on Longitudinal Citation Data.  The purpose of this paper was to experimentally explore a number of properties of community detection algorithms, especially as applied to citation networks (c.f. Supreme Court, Tax Court, United States Code).

The model is a fairly simple discrete-time directed growing graph.  At time 0, we create a completely disconnected graph with some initial number of vertices.  The number of new vertices after time 0 is then modeled by a homogeneous Poisson process.  For each of these new vertices, we also model the number of edges per vertex as IID Poisson distributions.  The probability distribution over these edges is specified by a modified preferential attachment mechanism.

Most of our questions consider what we’ve called the average pairwise stability.  This can be thought of as answering the following question: “what is the probability that Alice and Bob are friends tomorrow if they were friends today?” Here, friendship corresponds to sharing the same community.  By asking this question for all pairs of vertices (dyads) for all time steps in which both vertices existed, we get a probability that a community dyad is preserved from step to step.  It is important to note that we’re not claiming all algorithms applied to all systems should have high average pairwise stability. In fact, for systems that involve dynamics like random rewiring, the only way to get high pairwise stability is to put all vertices in the same community or their own community at all steps – obviously trivial and unhelpful solutions in practice.

Given this model and this conception of stability, we want to perform the following experiments:

1. How do the edge-betweenness, fast greedy, and leading eigenvector community detection algorithms compare in terms of their average pairwise stability…
   1. for varying levels of preferential attachment?
   2. for varying vertex and edge rates?
2. Is there a significant tradeoff between the number of communities and the average pairwise stability of these community detection algorithms?

The answers are yes, yes, and yes!  You should read the paper for more details.

I’ve also produced some code to help you assess the average pairwise stability for your dataset.  The code requires igraph and is only in Python at this point due to an issue with R’s vertex label handling (which I can hopefully work around).  You can get the average pairwise stability methods on github and check out the example below:

Related posts:

1. Stability of Community Detection Algorithms on Longitudinal Citation Data — Our New Paper on the Physics arXiv — {Click Below to Download}
2. New Paper: Properties of the United States Code Citation Network
3. Algorithmic Community Detection in Networks

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The cover story of this week’s Economist is entitled The Data Deluge. This is, of course, a favorite topic of the hosts of this blog. While a number of folks have already highlighted this trend, we are happy to see a mainstream outlet such the Economist reporting on the era of big data. Indeed, the convergence of rapidly increasing computing power, and decreasing data storage costs, on one side, and large scale data collection and digitization on the other … has already impacted practices in the business, government and scientific communities. There is ample reason to believe that more is on the way.

In our estimation, for the particular class of questions for which data is available, two major implications of the deluge are worth reiterating: (1) no need to make assumptions about the asymptotic performance of a particular sampling frames when population level data is readily available; and (2) what statistical sampling was to the 20th century, data filtering may very well be to the 21st ….

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1. Data.gov is Now Online
2. The Rise of the Data Scientist [From Flowing Data]
3. Real Time Visualization of US Patent Data [Via Infosthetics]