Mark Newman

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For the sculptor and illustrator, see Mark Newman (sculptor). For the American baseball executive, see Mark Newman (baseball).
Mark Newman
Born British
Residence United States
Fields Physics
Institutions University of Michigan
Santa Fe Institute
Alma mater Merton College, Oxford
Doctoral advisor David Sherrington

Mark Newman is a British physicist and Paul Dirac Professor of Physics at the University of Michigan, as well as an external faculty member of the Santa Fe Institute. He is known for his fundamental contributions to the fields of complex networks and complex systems, for which he was awarded the 2014 Lagrange Prize.

Career[edit]

Mark Newman grew up in Bristol, England. After graduating from high school he studied physics at Oxford University, where he went on to earn a PhD. Then he conducted postdoctoral research at Cornell University, where he collaborated with Steven Strogatz. After leaving Cornell, Newman took a position at the Santa Fe Institute, a private research institute in northern New Mexico devoted to the study of complex systems. At the Santa Fe Institute he collaborated with Duncan J. Watts and Cristopher Moore. In 2002, Newman moved from Santa Fe to the University of Michigan, where he is currently the Dirac Professor of Physics and a professor in the university's Center for the Study of Complex Systems.

Research[edit]

Newman is known for his research on complex networks, and in particular for work on collaboration patterns of scientists, random graph theory, assortative mixing, community structure, percolation theory, and network epidemiology.[1] He was also co-inventor, with Michael Gastner, of a new method for generating cartograms, used in the Internet-based Worldmapper. Their work gained attention following the 2004 US presidential election when it was used as the basis for a widely circulated map of the election results, which adjusted the size of states based on their population to give a more accurate sense of how many voters actually voted for each party.[2] Another version of the cartogram also included color shading based on how large the majority was in each region.[3]

Newman's paper "The structure and function of complex networks"[4] received the most citations of any paper in mathematics between 2001 and 2011.[5]

Newman's network-based methods have been applied to a variety of fields, including psychology, sociology, economics and biology. The same basic methods have accurately predicted a wide variety of results, from relationships between organisms in an ecosystem to associations between terrorist organizations.[6] Newman has also applied network analysis to the risk of forest fires[7] and the social behavior of dolphins in New Zealand,[8] as well as to the structure of the scientific community itself.[9]

Newman has also applied network theory to explain the common occurrence of power-law distributions in many real-world phenomena, including the distribution of wealth, the sizes of cities, and the frequency of words in languages (see Zipf's Law).[10] Newman has also developed new statistical methods of analyzing power-law distributions that are better suited to their unusual properties (compared with more standard statistical techniques that are generally based on normal distributions).[11]

See also[edit]

Selected publications[edit]

Books[edit]

  • J. J. Binney, A. J. Fisher, N. J. Dowrick, and M. E. J. Newman (1992). The Theory of Critical Phenomena. Oxford: Oxford University Press. 
  • M. E. J. Newman and G. T. Barkema (1999). Monte Carlo Methods in Statistical Physics. Oxford: Oxford University Press. ISBN 0-19-851796-3. 
  • Mark Newman, Albert-László Barabási, and Duncan J. Watts (2006). Structure and Dynamics of Networks. Princeton, NJ: Princeton University Press. 
  • Daniel Dorling, Mark Newman and Anna Barford (2008). The Atlas of the Real World. London: Thames & Hudson Ltd. ISBN 978-0-500-51425-2. 
  • M. E. J. Newman (2010). Networks: An Introduction. Oxford: Oxford University Press. ISBN 0-19-920665-1. 

Articles[edit]

Newman, M.E.J. (29 May 2006). "Power laws, Pareto distributions and Zipf's law". Contemporary Physics 46: 323-351. doi:10.1016/j.cities.2012.03.001. Retrieved 9 April 2015.  One of his papers on the latter subject was cited over 3000 times.Clauset, Aaron; Shazili, Cosma Rohila; Newman, M. E. J. (2 Feb 2009). "Power-law distributions in empirical data". SIAM Review 51: 661-703. doi:10.1137/070710111. 

References[edit]

  1. ^ Mark Newman's home page
  2. ^ Ehrenberg, Rachel (7 November 2012). "Red state, blue state". Science News. The Society for Science and the Public. Retrieved 8 April 2015. 
  3. ^ "Fifty shades of purple". Physics World. Institute of Physics. 12 November 2012. Retrieved 8 April 2015. 
  4. ^ Newman, Mark E. J. (June 2003). "The structure and function of complex networks". SIAM Review 45 (2): 167-256. doi:10.1137/S003614450342480. Retrieved 8 April 2015. 
  5. ^ "Top institutions in Mathematics". Times Higher Education. 2 June 2011. Retrieved 8 April 2015. 
  6. ^ Rehmeyer, Julie (2 June 2008). "Communities of communities of...". Science News. The Society for Science and the Public. Retrieved 8 April 2015. 
  7. ^ Ball, Phillip (27 February 2002). "COLD safer than HOT". Nature News. Nature. Retrieved 8 April 2015. 
  8. ^ "Circles of Friends". The Economist. 30 September 2004. Retrieved 8 April 2015. 
  9. ^ Ball, Phillip (12 January 2001). "Science is all about networking". Nature News. Nature. Retrieved 8 April 2015. 
  10. ^ Newman, M.E.J. (29 May 2006). "Power laws, Pareto distributions and Zipf's law". Contemporary Physics 46: 323-351. doi:10.1016/j.cities.2012.03.001. Retrieved 9 April 2015. 
  11. ^ Clauset, Aaron; Shazili, Cosma Rohila; Newman, M. E. J. (2 Feb 2009). "Power-law distributions in empirical data". SIAM Review 51: 661-703. doi:10.1137/070710111.