Mark E. J. Newman

Career Mark Newman grew up in Bristol, England, where he attended Bristol Cathedral School, and earned both an undergraduate degree and PhD in physics from the University of Oxford, before moving to the United States to conduct research first at Cornell University and later at the Santa Fe Institute. In 2002 Newman moved to 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.

Research Newman is known for his research on complex networks, and in particular for work on random graph theory, assortative mixing, community structure, percolation theory, collaboration patterns of scientists, and network epidemiology. In early work in collaboration with Steven Strogatz and Duncan Watts, he developed the theory of the configuration model, one of the standard models of network science, and associated mathematical methods based on probability generating functions. Around the same time he also popularized the concept of community structure in networks and the community detection problem, and worked on mixing patterns and assortativity in networks, both in collaboration with Michelle Girvan. In network epidemiology he published both on formal results, particularly concerning the connection between the SIR model and percolation, as well as practical applications to infections such as SARS, pneumonia, and group B strep. In later work he has focused on spectral graph theory and random matrices, belief propagation methods, and network reconstruction, among other things.

Newman has also worked on a range of topics outside of network theory in the general area of statistical physics, particularly on spin models and on percolation, where he is the inventor (with Robert Ziff) of the Newman-Ziff algorithm for computer simulation of percolation systems. Outside of physics he has published papers in mathematics, computer science, statistics, biology, ecology, epidemiology, paleontology, and sociology. He has worked particularly on so-called power-law distributions, which govern the statistics of a wide range of systems from human populations and earthquakes to spoken languages and solar flares. With Aaron Clauset and Cosma Shalizi, Newman developed statistical methods for analyzing power-law distributions and applied them to a wide range of systems, in various cases either confirming or refuting previously claimed power-law behaviors. In other work, he was also the inventor, with Michael Gastner, of a method for generating density-equalizing maps or cartograms. Their work gained attention following the 2004 US presidential election when it was used as the basis for a widely circulated set of maps of the election results.

Newman's work is unusually well cited. A 2019 Stanford University study ranked Newman as having the third highest citation impact of any active scientist in the world in any field, and the 28th highest of all time, out of 6.8 million scientists worldwide. In 2021 Newman was named a Clarivate Citation Laureate, a distinction that recognizes scientists who have had "research influence comparable to that of Nobel Prize recipients". In the ten years following its publication, Newman's 2003 paper "The structure and function of complex networks" was the most highly cited paper in the entire field of mathematics.

Awards and honors Newman is a Fellow of the Royal Society, Fellow of the American Physical Society, Fellow of the American Association for the Advancement of Science, Fellow of the Network Science Society, a Simons Foundation Fellow, and a Guggenheim Fellow. He was the recipient of the 2014 Lagrange Prize from the ISI Foundation, the 2021 Euler Award of the Network Science Society, the 2024 Leo P. Kadanoff Prize of the American Physical Society, and the 2026 John von Neumann Prize from the Society for Industrial and Applied Mathematics.