What do zebras fleeing a lion and emailing patterns after the Enron scandal was exposed have in common? Networks! Network theory can be used to identify exactly when a lion comes into the vicinity if a herd of zebras. Their pattern of reaction can be predicted from the specific way that the zebras interact with each other as determined by studies of zebra networks. Biologists have taken advantage of computer science methodology to analyze how associations within a population are made by using well kept records with zebras as nodes and the interaction between them as the edges. This research on zebras has been investigated by Dan Rubenstein of Princeton University, an ecologist who has studied zebras and other horse-like animals for 20 years. He initially embarked upon the use of network theories to potentially enlighten him on the differences between two species of zebras: the plains zebra, which are thriving, while the Grevy’s zebra are endangered. He discovered that the two species are very unalike in their networking behaviors. While Grevy’s zebra spend their time with different acquaintances every day, plains zebra generally form tight social groups with a few outliers that are not a part of the main group. Here is a representation of the herd of plains zebra that Rubenstein studied:

This example illustrates perfectly the reason for the need for such classes such as Networks. Such a systematic study of associations between nodes can be a very important analysis tool that can reveal characteristic that even a biologist cannot discover without the aid of a computer scientist. When Professor Rubenstien received this aid, by giving his data over to a computer scientist to analyze, the computer analyst found that the pattern of zebras fleeing from a lion resembled another networking scenario that she was working on: the circulation of email after the Enron scandal was revealed. This example resembles what we discussed in class about organizing information “as we think” instead of linearly. By doing so, interesting insights can be found from seemingly unrelated topics.