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By Elizabeth Gudrais 44 May - June 2010 Connected by Links (Also Called Ties)

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By Elizabeth Gudrais 44 May - June 2010 Connected by Links (Also Called Ties) NetworkedNicholas Christakis Exploring the weblike structures that underlie everything from friendship to cellular behavior f a campaign volunteer shows up at your door, urging tools for predicting stock-price trends, designing transportation Iyou to vote in an upcoming election, you are 10 percent more systems, and detecting cancer. likely to go to the polls—and others in your household are 6 It used to be that sociologists studied networks of people, percent more likely to vote. When you try to recall an unfamiliar while physicists and computer scientists studied different kinds word, the likelihood you’ll remember it depends partly on its po- of networks in their own fields. But as social scientists sought to sition in a network of words that sound similar. And when a cell understand larger, more sophisticated networks, they looked to in your body develops a cancerous mutation, its daughter cells physics for methods suited to this complexity. And it is a two- will carry that mutation; whether you get cancer depends largely way street: network science “is one of the rare areas where you on that cell’s position in the network of cellular reproduction. see physicists and molecular biologists respectfully citing the However unrelated these phenomena may seem, a single schol- work of social scientists and borrowing their ideas,” says Nicho- arly field has helped illuminate all of them. The study of networks las Christakis, a physician and medical sociologist and coauthor can illustrate how viruses, opinions, and news spread from per- of Connected: The Surprising Power of Our Social Networks and How They son to person—and can make it possible to track the spread of Shape Our Lives (2009). obesity, suicide, and back pain. Network science points toward The basic elements of a network are simple: it consists of nodes by Elizabeth Gudrais 44 May - June 2010 connected by links (also called ties). But as the numbers of nodes With data from the Framingham Heart Study, under way since and links increase, the number of possible configurations grows 1948, they mapped more than 50,000 social ties among 5,124 peo- exponentially. Likewise, there are innumerable possibilities for ple (who connected outward in turn to a network of more than what a node and a link can represent: a word, a gene, or a person, 12,000 people). Because the study tracked all manner of health in the first case; phonetic similarity, coincident expression, or a markers and asked subjects about an exhaustive list of behav- conversation, in the second. Structurally simple, yet analytically iors—diet and exercise, medications, recreational substance use, incredibly complex, networks hold the answers to so many ques- emotions—it was a rich lode of data. tions that at Harvard alone, the number of researchers studying The two men started publishing their findings with a splash: them may reach three digits. Here is a sampling of the newest a 2007 article in the New England Journal of Medicine reporting that work in this dynamic field. obesity spreads through social networks, as people are apparently influenced by friends’ weight gain to become obese themselves. “STUFF SPREADS” IN MYSTERIOUS WAYS More perplexing is their finding that obesity spreads through up Christakis, professor of medicine and medical sociology at to three degrees of separation. If a subject named a friend who Harvard Medical School (HMS) and professor of sociology in was also in the study, and that friend’s friend became obese, the the Faculty of Arts and Sciences, and University of California first subject’s chances of becoming obese were roughly 20 per- political scientist James H. Fowler ’92, Ph.D. ’03, wrote Connected cent greater. Across one more degree of influence (husband’s after discovering that each was working on a special case of net- friend’s friend or friend’s sibling’s friend—i.e., three degrees work effects (the effect of a spouse’s death on one’s own health, away), the risk was 10 percent greater. Weight gain appears to for Christakis; the spread of voting behavior, for Fowler) and re- ripple through friend groups via some unseen mechanism such as alizing they shared an interest in what else could be spreading altered eating or exercise behavior, or adjustment of social norms through networks. regarding weight. The book is an exuberant romp through the field, presenting The authors found similar patterns for happiness, loneliness, findings from medicine, epidemiology, evolutionary biology, so- depression, alcohol consumption, the decision to stop smoking, ciology, anthropology, political science, economics, mathemat- and even divorce. “Our health depends on more than our own ics, and beyond. The authors discuss the spread of biology or even our own choices and actions,” they write in Con- laughter, tastes in music, sexual behavior, and anx- nected. “Our health also depends quite literally on the biology, iety over nut allergies. They note one study that choices, and actions of those around us.” rigorously compared the structure of networks of Visit harvardmag. myriad phenomena and found a strong similarity com/extras to see a video of slime between the bill-sponsoring patterns of U.S. sena- mold solving tors and social licking in cows. They report that a laboratory Physarum polycephalum—slime mold—is more effi- maze, and one of cient than Japanese graduate students in finding mold forming a Japanese railway the shortest route through a maze (the fungus can system map “collaborate” by fanning out in the form of a net- work to explore all possible paths); and share Japanese mycolo- gist Toshiyuki Nakagaki’s follow-up studies, in which the fungus was as good as or better than humans at devising maps for rail- way systems in Great Britain and Japan. These studies, they say, demonstrate the problem-solving power inherent in networks. These wide-ranging, sometimes wacky findings reflect the field today. The boundaries between disciplines are becoming all but meaningless in network analysis; Christakis’s lab group includes scholars of physics, economics, anthropology, computational bi- ology, sociology, and healthcare policy. “Often new knowledge is produced at the intersection of disciplines,” he says, “and in network science this is happening in spades.” But the core of the Christakis-Fowler collaboration is origi- nal research on what spreads through human social networks. OWLER THIS MAP is a typical example of the computer-generated images used F to help understand networks and network effects. A subset of the “obesity D JAMES JAMES D network” mapped by Nicholas Christakis and James Fowler, the image shows n 2,200 subjects from the Framingham Heart Study (from a total of 12,067). Each dot, or “node,” represents one person (red borders indicate women; blue, men). The yellow dots represent obese people—those with a body mass index (BMI) of 30 or more—and node sizes are proportional to BMI. Colors of “ties,” or links between nodes, indicate relationship type: purple for friend or spouse, A CHRISTAKIS HOLAS c orange for family. Note the visible clusters of obese people; Christakis and Fowler report that network effects—in which one subject’s weight gain influ- ences the BMI of those around him—help to explain these obesity clusters. COURTESY OF NI OF COURTESY Portraits by Stu Rosner Harvard Magazine 45 INNOVATION AT THE INTERSECTION any people want (Gatekeepers in the diagram YEDA RESEARCH to achieve success as INSTITUTE include Prakash Jagtap, who Minventors; few actu- worked as a scientist at Har- ally do. Lee Fleming, Weath- vard Medical School in 2001 erhead professor of business and 2002, and now directs drug administration at Harvard discovery at the Lexington, Business School, is studying Massachusetts, firm Inotek; and Lieber PALO ALTO whether someone’s position RESEARCH CENTER Thomas Rueckes, Ph.D. ’01, co- Rueckes in a network seems to mat- NANTERO founder and chief technology ter when it comes to deter- officer of Nantero in Woburn, mining who succeeds. Massachusetts.) Using data on all U.S. pat- INFINEON The database also enabled ents since 1975, his team Whitesides tracing inventor mobility, from mapped innovation networks firm to firm or university to for inventors across the university, across the last 35 country. The network of Har- years. Fleming and Matthew Jagtap vard inventors who applied Marx, M.B.A. ’05, D.B.A. ’09 for patents between 2003 (now an assistant professor and 2008 is shown here, with at MIT’s Sloan School of Man- larger nodes indicating that agement) determined that an inventor’s patents are fre- statewide enforcement of quently cited, i.e., influential; a VERTEX noncompete clauses—where connection between nodes companies bar employees from indicates collaboration on working for a competitor for a an invention, measured when set period of time after leaving the inventors list themselves ROnALD LAI AnD ALEX D’AMOUR, InSTITUTE FOR QUAnTITATIVE SOcIAL ScIEncE the employer in question—in- jointly on a patent application. Trademark Office to create a new subclass. duce brain drain. States that enforce such At a basic level, this map illustrates the Comparing the models of the “broker”— clauses are particularly likely to lose their global connections of Harvard inven- an influential person connected to many most productive and well-connected in- tors. They collaborate not only with each others who don’t know each other—and ventors, for whom opportunities in other other, but with the distinguished Palo Alto the “connector”—an influential individual states are easy to come by. An effort is un- Research Center in California; the multi- with a habit of introducing his collabora- der way to change Massachusetts law to national General Electric; and the Yeda Re- tors to each other—he found that brokers prohibit the enforcement of these claus- search Institute in Israel.
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