by Paul Mannix

Brazilian hooker-john hookups used for network analysis
[Via Ars Technica]

Modern communication networks, such as cell phone systems and the Internet, have provided researchers with the opportunity to study human associations and movement on a much greater scale than previously possible. Almost all of the papers that describe this sort of network analysis notes that it could have real world applications, since existing and emerging disease threats can spread through social and transit networks. A paper that will be released later this week by PNAS, however, skips the whole “this may be a useful model” aspect, and goes straight to a network in which diseases actually do spread: prostitutes and their clients.

Although organized prostitution is apparently illegal in Brazil, there are no laws against receiving payment for sex, making it possible for sex workers to freelance. Like everything else these days, that trade has found its way onto the Internet, and some enterprising Brazilians created an ad-supported public forum for individuals on both sides of the transaction. The forum is heavily moderated to keep it strictly on-topic: sellers (aka prostitutes) can advertise their business, and those that partake can rate the experience, as well as provide some information about the precise services rendered (the focus was strictly on heterosexual prostitution in this system).

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Using the data generated by Web 2.0 technologies these researchers have been able to garner a lot of insight into a very large social network that has existed for some time.

This looks like it will be a pretty interesting article – Information dynamics shape the sexual networks of Internet-mediated prostitution. And you can download it for free.

These online forums map very well with the correlated social networks, providing a nice insight into how the networks are set up and how something like diseases might progress through the network.

It is also a network that is highly optimized to move information around – who is the best for doing whatever at whichever price. It is also a very large network, so they were able to identify some other interesting characteristics.

For example, social networks also alter over time. Because they had 6 years worth of data, the researchers could examine how the contacts changed over time. They found that there were still very large connected networks at all times, with a minimum of 71% of the people being connected in the network.

There were over 10,000 buyers and about 6600 sellers. The average number of jumps between buyers was about 5.8 (those 6 degrees of separation) while it was smaller for sellers (about 4.9). Also interestingly, was the high number of what are called four-cycles – a set of connections that end where they start. These are normally described as a mutual friend introducing two people, this creating a triangle. This seems to make sense to me – someone who has found a great prostitute telling his friends, for example.

Another interesting aspect of the network, and one that has implications for disease spread, is that it was slightly disassortative. In a highly assortative network, highly connected members also tend to connect to each other. In a disassortative network, highly connected members tend to connect to less highly connected members.

The data suggest that for this network the most active buyers, those with the most connections to prostitutes, tended to connect to prostitutes that were less active in the network (i.e. fewer connections). And the most popular sex workers tended to connect to buyers that were not actively seeking out other prostitutes.

This actually creates a network where disease is not likely to arise but when it does, it could spread to a larger part of the network.

Another intriguing observation they made is that on a log-log plot, the number of sex workers and buyers increases linearly as the size of the city increases. In many things (such as wealth or information workers), the trend is greater than linear because larger cities provide greater benefits. Linear scaling falls for things that are usually necessities, such as water or power.

Normally, prostitution requires face-to-face interactions, so being in a big city, with its increasing large social networks, makes it easier to find one. And thus harder to find one in a small town. But, the online form removes that need and now small towns can do just as well as large towns, bringing prostitution down to the level of human necessities.

Pretty nice examination of a somewhat specialized human social network, one that could only really be studied because of Web 2.0 technologies.