Mark Newman

Mark Newman is a British physicist and Anatol Rapoport Distinguished University 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

Mark Newman grew up in Bristol, England and earned both an undergraduate degree and a 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, a private research institute in northern New Mexico devoted to the study of complex systems. 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 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 method for generating density-equalizing maps or cartograms, which forms the foundation for the Worldmapper web site. 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 voted for each party.^[2][3]

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.^[4] Newman has also studied the risk of forest fires^[5] and the social behavior of dolphins in New Zealand,^[6] as well as the structure of the scientific community itself.^[7]

Newman has worked on power-law distributions in complex systems, including in the distribution of wealth, the sizes of cities, and the frequency of words in languages (see Zipf's Law).^[8] With collaborators Aaron Clauset and Cosma Shalizi, Newman developed statistical methods for analyzing power-law distributions and applied them to the study of a wide range of systems, in various cases either confirming or denying the existence of previously claimed power-law behaviors.^[9]

Newman's paper "The structure and function of complex networks"^[10] received the most citations of any paper in mathematics between 2001 and 2011.^[11]

Awards and honors

In 2007, Newman was elected as a Fellow of the American Physical Society (APS).^[12] In 2011 and 2012, he received a Faculty Recognition Award and an Excellence in Education Award, both from the University of Michigan. In 2014, he was elected as a Fellow of the American Association for the Advancement of Science (AAAS), received the 2014 Lagrange Prize from the ISI Foundation, and was the fifth recipient of the Zachary Karate Club CLUB prize.^[13] In 2016, he was elected as a Simons Fellow in Theoretical Physics and received a Guggenheim Fellowship.^[14]

See also

• Complex network
• Social network
• Random graph
• Assortative mixing
• Community structure
• Percolation theory
• Cartogram

Selected publications

Books

• J. J. Binney; A. J. Fisher; N. J. Dowrick & M. E. J. Newman (1992). The Theory of Critical Phenomena. Oxford: Oxford University Press. 
• M. E. J. Newman & G. T. Barkema (1999). Monte Carlo Methods in Statistical Physics. Oxford: Oxford University Press. ISBN 0-19-851796-3. 
• Mark Newman; Albert-László Barabási & Duncan J. Watts (2006). Structure and Dynamics of Networks. Princeton, NJ: Princeton University Press. 
• Daniel Dorling, Mark Newman & 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

• M. E. J. Newman (2001). "The structure of scientific collaboration networks". Proceedings of the National Academy of Sciences. 98 (2): 404–409. arXiv:cond-mat/0007214. Bibcode:2001PNAS...98..404N. doi:10.1073/pnas.021544898. PMC 14598. PMID 11149952. 
• M. E. J. Newman; S. H. Strogatz; D. J. Watts (2001). "Random graphs with arbitrary degree distributions and their applications". Physical Review E. 64 (2): 026118. arXiv:cond-mat/0007235. Bibcode:2001PhRvE..64b6118N. doi:10.1103/PhysRevE.64.026118. 
• M. E. J. Newman (2002). "Assortative mixing in networks". Physical Review Letters. 89 (20): 208701. arXiv:cond-mat/0205405. Bibcode:2002PhRvL..89t8701N. doi:10.1103/PhysRevLett.89.208701. PMID 12443515. 
• M. E. J. Newman (2003). "The structure and function of complex networks". SIAM Review. 45 (2): 167–256. arXiv:cond-mat/0303516. Bibcode:2003SIAMR..45..167N. doi:10.1137/S003614450342480. 
• M. T. Gastner; M. E. J. Newman (2004). "Diffusion-based method for producing density equalizing maps". Proceedings of the National Academy of Sciences. 101 (20): 7499–7504. arXiv:physics/0401102. Bibcode:2004PNAS..101.7499G. doi:10.1073/pnas.0400280101. PMC 419634. PMID 15136719. 
• M. E. J. Newman (2006). "Modularity and community structure in networks". Proceedings of the National Academy of Sciences. 103 (23): 8577–8582. arXiv:physics/0602124. Bibcode:2006PNAS..103.8577N. doi:10.1073/pnas.0601602103. PMC 1482622. PMID 16723398. 
• MEJ. Newman (2005), Power laws, Pareto distributions and Zipf's law, Contemporary Physics, Sep/Oct 2005, Vol. 46 Issue 5, p323-351, 29p; doi:10.1080/00107510500052444. http://arxiv.org/PS_cache/cond-mat/pdf/0412/0412004v3.pdf
• Newman, Mark E. J. (June 2003). "The structure and function of complex networks". SIAM Review. 45 (2): 167–256. arXiv:cond-mat/0303516. Bibcode:2003SIAMR..45..167N. doi:10.1137/S003614450342480. Retrieved 8 April 2015. 
• Clauset, Aaron; Moore, Christopher; Newman, M.E.J. (1 May 2008). "Hierarchical structure and the prediction of missing links in networks". Nature. 453: 98–101. arXiv:0811.0484. Bibcode:2008Natur.453...98C. doi:10.1038/nature06830. Retrieved 8 April 2015. 
• Newman, M.E.J. (29 May 2006). "Power laws, Pareto distributions and Zipf's law". Contemporary Physics. 46: 323–351. arXiv:cond-mat/0412004. doi:10.1016/j.cities.2012.03.001. 
• Clauset, Aaron; Shazili, Cosma Rohila; Newman, M. E. J. (2 Feb 2009). "Power-law distributions in empirical data". SIAM Review. 51: 661–703. arXiv:0706.1062. Bibcode:2009SIAMR..51..661C. doi:10.1137/070710111. 

References