Cyberspace

The over-exercised term “networking” seems to be in wider use because it can be applied to many things. For example, your brain and nervous system are a network; the behavior of certain “social” insects (bees, ants, etc.) is a form of networking; the interaction between objects in the solar system can be described as a network; the relationship between people’s political leanings and the books they buy or the blogs they read can be modeled as a network.

Researchers at the University of Michigan have developed algorithms to analyze networks to detect trends and predict behavior.

New analysis of networks reveals surprise patterns in politics, the web

When analyzing buyers of political titles purchased through Amazon, they found this interesting relationship:

For instance, researchers used the algorithm to sort books sold on Amazon.com into left- and right-wing groups, and they found the book most appealing to conservatives was actually written by Democrat Zell Miller.

Miller, the former governor of Georgia and U.S. senator, angered Democrats by endorsing George Bush during the last presidential election. Miller’s book, “A National Party No More, The Conscience of a Conservative Democrat,” was the book most central to the community of conservative book buyers, according to researchers.

When analyzed using Newman’s method [associate professor Mark Newman, who developed the technique], the network of books separated into four communities, with dense connections within communities and looser connections between them. One community was composed almost entirely left-wing books, and the other almost entirely of right-wing ones. Centrist books comprised the other two categories. The computer algorithm doesn’t know anything about the books’ content—it draws its conclusions only from the purchasing patterns of the buyers—but Newman’s analysis seems to show that those purchasing patterns correspond closely with the political slant of the books.

When it comes to political blogs, the algorithm shows that we tend to link to like-minded blogs while seldom crossing over to the other side:

In another example, Newman used the algorithm to sort a set of 1225 conservative and liberal political blogs based on the network of web links between them. When the network was fed through the algorithm, it divided cleanly into conservative and liberal camps. One community had 97 percent conservative blogs, and the other had 93 percent liberal blogs, indicating that conservative and liberal blogs rarely link to one another. In a further twist, the computer analysis was unable to find any subdivision at all within the liberal and conservative blog communities.

Now, I am certainly not qualified to analyze the psychology of this behavior, but I do know that people tend to gravitate towards the set of values and ideas that they hold as their own. I am not sure that I find this tendency “surprising.”