Data Science/Computational Social Science Seminar: Mark Newman
Ehrlicher Room, 3100 North Quad
Patterns and surprises in rich but noisy network data
There have been many empirical studies in recent years of the structure of networked systems such as the World Wide Web, citation networks, social networks, and biological networks such as metabolic networks and food webs. Observations of networks like these are often noisy, containing measurement error, contradictory observations, or missing data, but they can also be richly structured, with measurements of different types, repeated observations, annotations, or metadata. In this talk I will address the problem of making best estimates of network structure from such rich but noisy data, particularly focusing on social and biological examples. In the process, we will see that the pattern of errors in network data is far from random and can teach us some intriguing lessons not only about the data but also about the underlying systems they describe.
Mark Newman received a PhD in physics from Oxford University in 1991 and conducted postdoctoral research at Cornell University before taking a position at the Santa Fe Institute in 1996. In 2002 he left Santa Fe for the University of Michigan, where he is currently the Anatol Rapoport Distinguished University Professor of Physics and a professor in the university's Center for the Study of Complex Systems. Among other honors, Professor Newman is a Fellow of the American Association for the Advancement of Science and the American Physical Society, he has been a Simon's Foundation Fellow and a Guggenheim Fellow, and was winner of the 2014 Lagrange Prize, the largest international prize for research on complex systems. He is the author of over 150 scientific publications and seven books, including "Networks", an introduction to the field of network theory, and "The Atlas of the Real World", a popular book on cartography.