For most people, the term “social network” conjures up online communities like Facebook and Twitter. But scientists use the term more generally to describe their studies of social interactions within groups of people or even animals.

A new field, commonly called network science, has emerged in recent years. It is a highly interdisciplinary branch of physics and applied math, providing collaborators such as public health researchers, sociologists, and marine biologists with computer modeling and other tools that can probe and analyze data more thoroughly and accurately than before. Mark Newman, the University of Michigan's Paul Dirac Collegiate Professor and a professor in the Center for the Study of Complex Systems, is one of its founders.

While the math behind network science is certainly complex, its basic premise is quite simple. All networks—between computers, people, or animals—can be broken down and examined as a pattern of relationships between the individuals in the network. Each individual in the group can be represented by a point, and each relationship by a line. In the jargon of the field, the points and lines are called “nodes” and “edges.” Network scientists use sophisticated computer modeling and other statistical techniques to create images and diagrams using data that has been collected about nodes and how they relate to each other. By examining and mapping the inner core of each and every relationship, network scientists can bring more insight and accuracy to analyzing data than traditional statistical methods allow.

Not only does this painstaking analysis explain a network’s behavior in the present, but it is even more valuable in making highly accurate predictions about the future as well. “We study social networks because we want to understand and perhaps predict the behavior of communities and societies,” explains Newman. Network scientists have studied terrorist networks and Newman himself collaborates with Betsy Foxman, an epidemiologist in the U-M School of Public Health, to better understand the spread of serious diseases like HIV and Group B streptococcus.

A Collaborative Science

Probably because of its “real world” applications, network science is seeing an explosion of interest—Newman estimates there were only about half a dozen network scientists in 1998, when he started, and now there are a thousand or more worldwide. The field began to gain traction after the publication of an influential paper in the journal Nature in 1998 by physicist Duncan Watts and mathematician Steven Strogatz titled “Collective Dynamics of Small-world Networks.”

Newman began studying networks at the Santa Fe Institute, collaborating with Watts, but soon moved to U-M, attracted, he says, by the University???s strong tradition of support for interdisciplinary science. “Network science is highly collaborative and that’s why I came here,” Newman says.

In addition to social networks, Newman describes three other broad categories of networks: technical (such as the internet, phone systems, and airline routes); informational (such as the world wide web); and biological (such as metabolic systems and neural networks). Newman has made important contributions in all four areas, including work on the Internet and the web, email networks, and ecological networks, as well as networks in college football and the U.S. Congress.

While Newman is a prolific researcher, he is also a greatly admired and highly dedicated teacher. A soft-spoken man with British accent and manners, he seems uncomfortable talking about his achievements, which include the 2010 publication of Networks, An Introduction (Oxford 2010), a popular textbook that explains this complex science in readable terms.

The science of networks is becoming more compelling now that scientists in all fields are beginning to understand how everyone and everything is interconnected to some degree. One of a network scientist’s main tasks is to determine which of these connections are significant, and why, through interpretation and analysis of the patterns that emerge from their computer models.

While the six-degrees myth greatly oversimplifies the science that originated with Harvard psychologist Stanley Milgram in the 1960s, there is some truth in it. Research reveals that any person can reach almost any other via a small number of intermediate acquaintances. The “small world” phenomenon also applies to computer networks, where it allows any computer to connect to any other by making only a small number of “hops” across the Internet. “Without this effect, the Internet would not work nearly as well as it does, and perhaps wouldn't work at all,” Newman says. He and other collaborators have also studied the patterns of bottlenecks and weak points in computer networks, to understand where they are vulnerable and how they can be made more robust against failure or hackers.

Some network science ideas are already changing our society—for instance in the area of marketing. “Recommendation systems on Amazon and Netflix use network ideas embedded in their software,” explains Newman. “That creates more opportunities to learn about [products] available to us.” But Newman also points out that network effects have been shown to influence consumer behavior as well, as people tend to buy the music or books that are recommended by the most people, without necessarily making an independent judgment for themselves. According to Newman, “once a product becomes popular, more people will buy it just because it's popular.”

Newman says he enjoys his work immensely because he can contribute to understanding problems across a wide range of disciplines—all of which have the potential to help make a positive difference in the world. Newman adds that he hopes to help the general public understand the brave new world beyond Facebook. “The study of social networks and networks in general is now a field that a lot of people are interested in,” he says. “Many physicists have difficulty explaining their work at parties, but with my work now, everybody seems to understand what we aim to accomplish.”