Graph Nerds of Boulder

A map of subreddits connected, not by the obvious related reddits you might spot on a sidebar, but actual reddit user behavior. For eight months, their algorithm tracked where users posted across the network. And given enough of a trend––say, 1,000 people regularly posted to both r/diablo and r/starcraft––the team made a connection between those subreddits on their map.

Analysts believe that insurgents in Afghanistan form similar networks to street gangs in the US. So the software for analysing these networks abroad ought to work just as well at home, say military researchers

Can cultural factors be more important than censorship in shaping Chinese surfing habits? Two researchers argue that a new study of the way global websites cluster together supports this idea

No description needed.

 I'm not a college sports guy, but I do appreciate a good topic map that breaks down the relationships. Seems that I see "Bulldogs" everywhere I go and now I know why. >[A map of] mascots for more than 1,100 schools from Division I to Division III. Unsurprisingly, a good deal of real estate is dedicated to animals. It’s there you’ll find the Wolverines (in the "weasel family" subcategory of the "mammals" section), along with big names like the Minnesota Golden Gophers and some lesser-known ones like the Rhodes College Lynx. Bulldogs and Tigers are both significant hubs, though the Eagle looks to be the most popular mascot of all, with 60 schools claiming it as their avatar.

 Being based in Boulder, altitude sickness is something we deal with when friends and relatives come to visit. Usually it's not acute, thankfully, but even minor altitude sickness is no fun. Here is some new information coming out about the disease thanks to Graph Theory! >Acute mountain sickness–or altitude sickness–is that common feeling of headache, dizziness, sleep disturbance and so on, that many people experience when travelling from lowland to high altitude. >Kenneth Baillie at the University of Edinburgh [...] used network theory to analyse the correlations between symptoms from a large number of people. The clusters in this network correspond to different diseases they say. The result could have an important impact on the way these diseases are treated and for the outcomes for victims.

This looks to be an excellent example of applying network theory to a problem outside of the usual areas of practice and finding some neat results.  For example, the abstract says "Our research shows how a large network in the southern Southwest grew and then collapsed, whereas networks became more fragmented in the northern Southwest but persisted."

In many online social systems, interactions between individuals are observable but their friendships are hidden. Using behavioral data from 18 billion interactions among 17 million individuals and an anonymous online survey, we develop highly accurate predictors that automatically extract latent friendships between individuals with over 95% accuracy. Using these predictors we infer the hidden social network of individuals in the online game Halo: Reach and find that its structure is recognizably different from the popular online social network, Facebook. These results clarify the nature of friendship in online social environments and suggest new opportunities and new privacy concerns for friendship-aware applications that do not require the disclosure of private friendship information.

 If you're into the Python, you'll wan to check this out. >graph-tool is an efficient python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers a level of performance which is comparable (both in memory usage and computation time) to that of a pure C/C++ library.

 This article is a nice overview of *Marriage Problems* on Bipartite Graphs, the algorithms for solving these problems, and variations you may encounter. We discussed several meetings ago how the [latest Nobel Prize in economics](http://www.nobelprize.org/nobel_prizes/economics/laureates/2012/press.html) was awarded for the application of this class of problems in optimizing various situations.

The most important thing the graph (and the paper) express is that while globalization (as a result of travel and the Internet) is real it follows a non-uniform pattern.  While this is self-evident on reflection it is often an idea that is overlooked in conversations about global society.

This article has discussed one of the approaches we took for compacting directed graph data in the context of one of our more recognizable data sets – deeply personalized genres.

>Alumni connections are important resources that contribute to university evaluation. Even though alumni connections represent networks, they have been mostly evaluated as tabular data (e.g. by providing average salary, employment rate, etc.). This ironically disregards all qualities of a network, from which an alumni network gets its name.

GraphPrism visually summarizes arbitrarily large graphs through combinations of “facets,” each presenting a multi-scale histogram of a single node- or edge-specific metric (e.g., degree, transitivity, conductance)

This is one of the more detailed papers I have seen recently, with some great information about classification of social networks.  What I find fascinating, though, is the discussion of network generation and the change over time.  There are still a number of open questions regarding how networks form and it is neat to see a paper (and a data set) explore that aspect.

Giland Lotan (@gilgul) posted this wonderful discussion about a presentation he gave on social network analysis and mapping communities. Be sure to check out the presentation itself at the bottom of the post, along with his github repository for all his python code.

I think this is a good example of a novel use of network theory to examine some quick hit questions. Link to the paper: http://users.uom.gr/~achat/papers/ICSE2012.pdf