Scientists are developing a new branch of network theory to understand zebra communities
Dan Rubenstein of Princeton University is an ecologist who has studied zebras and other horse-like animals for 20 years. To understand their social structure, he makes graphs of their interactions, with a separate graph for each season. Each zebra corresponds to a node on a graph (drawn as a dot), and two zebras are connected by a line if they've come within a few feet of each other during a given season. Rubenstein first graphed data showing the interactions among Grevy's zebras and among onagers, a closely related horse-like species. Both are fission-fusion societies without stable harems, so he expected the graphs to look similar. But in fact, the zebra graph had masses of tightly connected clumps, whereas the onager graph sprawled across the page, with looser and more random connections. The difference showed that the Grevy's zebras tended to hang out in cliques, whereas the onagers spent time with different buddies on different days.
Rubenstein was thrilled to see that network theory revealed patterns he wouldn't have seen otherwise, and he started thinking of all the other questions network theory might help him answer. In a harem society, is there a change in the zebras' interactions just before a bachelor overthrows a stallion in a coup? When zebras flee a lion, how do they decide which zebra will lead the stampede? Do different zebras play different roles in society? He rapidly hit a roadblock, however. Answering these questions required seeing how the interactions changed over time, but two zebras were connected in a graph if they had interacted at any time over a three-month season. The graphs didn't depict any of the changes in interactions within a season, so they couldn't answer the questions that most interested him. "It was like looking at the data with a very cloudy lens," Rubenstein says. "We were throwing away a lot of information, which was really frustrating." So he turned to Tanya Berger-Wolf, a computer scientist at the University of Illinois in Chicago, to find out how to analyze changing networks. She told him that the methods hadn't been developed yet. "If there's nothing else out there," she said, "I guess we'll have to do it." In August, Berger-Wolf and her collaborators received a $900,000 grant from the National Science Foundation for the project. First, Berger-Wolf is redefining the most basic concepts of network theory to make them work in a graph that reflects changes over time. Even the definition of a community has to be changed for dynamic networks. To take a very simple example, suppose that two zebras always hang out together, while a third is with them half the time. Is the third zebra a part of the group or not? If the third zebra joins the first two halfway through the study period, and then spends time with them consistently, we'd probably want to say yes. If the third zebra comes and goes, we might say no. She presented her new methods in August at the International Conference on Knowledge Discovery and Data Mining in San Jose, Calif.