In most empirical studies of networks, it is assumed that the data we collect accurately reflect the true structure of the network, but in practice this is rarely true: most network data are noisy, containing measurement error, false positives, false negatives, contradictory observations, or missing data. On the other hand the data can also be richly structured, with measurements of different types, repeated observations, annotations, or metadata. This talk will address the problem of making best estimates of network structure from such rich but noisy data, with a variety of example applications in social and biological networks. 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 his PhD in physics from Oxford University in 1991 and conducted postdoctoral research at Cornell University before taking a position at the Santa Fe Institute, a think-tank in New Mexico devoted to the study of complex systems. 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, a Fellow of the American Physical Society, and a Fellow of the Network Science 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.