Lada Adamic

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Lada Adamic is an American scientist who studies networks and how information moves online. She looks at how the structure of a network affects the spread of ideas, how information changes networks, and how people share knowledge on the web.

Adamic was a director of research at Facebook, where she led a team that studied computational social science. Before Facebook, she was an associate professor at the University of Michigan until 2013 and worked at Hewlett-Packard’s Information Dynamics Lab on projects about networks built from large data sets. She grew up in New York City and Colorado, attended Stuyvesant High School and Fairview High School, and earned a bachelor’s degree in physics, engineering and applied science at Caltech in 1997. She received her Ph.D. in applied physics from Stanford in 2001. While at Caltech and Stanford, she worked on early projects about the Internet’s growth and search processes and collaborated with Xerox PARC researchers.

Her research has covered many topics in online networks. She studied how Twitter was used during the 2010 U.S. elections, explored cultural differences in how people ask questions on Q&A tools, and looked at how families communicate on Facebook. She and colleague Eytan Adar hold a patent on classifying user profiles by web usage. She also has patents related to caching information based on its value.

Adamic’s work shows that weak ties—connections with fewer mutual friends—can spread information more effectively than strong ties. This insight helps explain how news and opportunities spread online. She has received several honors, including the National Science Foundation CAREER Award, the Henry Russel Award, and the Lagrange Prize in Complex Systems, and she was named a fellow of the Network Science Society in 2021. Her research has earned best-paper awards at major conferences, and her paper “Tracking Information Epidemics in Blogspace” was recognized as an influential work of its decade. She is an editor for Network Science and has taught an online course on Social Network Analysis.