The world is full of complicated networks that scientists would like to better understand---human social systems, for example, or food webs in nature. But discerning patterns of organization in such vast, complex systems is no easy task.
"The structure of those networks can tell you quite a lot about how the systems work, but theyre far too big to analyze by just putting dots on a piece of paper and drawing lines to connect them," said Mark Newman, an assistant professor of physics and complex systems at the University of Michigan.
One challenge in making sense of a large network is finding clumps---or communities---of members that have something in common, such as Web pages that are all about the same topic, people that socialize together or animals that eat the same kind of food. Newman and collaborator Michelle Girvan, a postdoctoral fellow at the Santa Fe Institute in Santa Fe, New Mexico, have developed a new method for finding communities that reveals a lot about the structure of large, complex networks. Newman will discuss the method and its applications Feb. 15 at the annual meeting of the American Association for the Advancement of Science in Seattle.
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