Mark Newman Anatol Rapoport Distinguished University Professor of Physics Department of Physics and Center for the Study of Complex Systems University of Michigan External Faculty Santa Fe Institute

Our group conducts research on the structure and function of networks, particularly social and information networks, which we study using a combination of empirical methods, analysis, and computer simulation. Among other things, we have investigated scientific coauthorship networks, citation networks, email networks, friendship networks, epidemiological contact networks, and animal social networks; we've studied fundamental network properties such as degree distributions, centrality measures, assortative mixing, vertex similarity, and community structure, and made analytic or computer models of disease propagation, friendship formation, the spread of computer viruses, the Internet, and network navigation.

Information

• Publications
• Recent presentations
• Press coverage
• Curriculum vitae

Selected publications:

1. The structure of scientific collaboration networks, M. E. J. Newman, Proc. Natl. Acad. Sci. USA 98, 404-409 (2001).
2. Random graphs with arbitrary degree distributions and their applications, M. E. J. Newman, S. H. Strogatz, and D. J. Watts, Phys. Rev. E 64, 026118 (2001).
3. Assortative mixing in networks, M. E. J. Newman, Phys. Rev. Lett. 89, 208701 (2002).
4. The structure and function of complex networks, M. E. J. Newman, SIAM Review 45, 167-256 (2003).
5. Modularity and community structure in networks, M. E. J. Newman, Proc. Natl. Acad. Sci. USA 103, 8577-8582 (2006).
6. Hierarchical structure and the prediction of missing links in networks, A. Clauset, C. Moore, and M. E. J. Newman, Nature 453, 98–101 (2008).
7. Power-law distributions in empirical data, Aaron Clauset, Cosma Rohilla Shalizi, and M. E. J. Newman, SIAM Review 51, 661-703 (2009).
8. Graph spectra and the detectability of community structure in networks, Raj Rao Nadakuditi and M. E. J. Newman, Phys. Rev. Lett. 108, 188701 (2012).
9. Percolation on sparse networks, Brian Karrer, M. E. J. Newman, and Lenka Zdeborová, Phys. Rev. Lett. 113, 208702 (2014).
10. Structure and inference in annotated networks, M. E. J. Newman and Aaron Clauset, Nature Communications 7, 11863 (2016).
11. Aspirational pursuit of mates in online dating markets, Elizabeth E. Bruch and M. E. J. Newman, Science Advances 4, eaap9815 (2018).
12. Network structure from rich but noisy data, M. E. J. Newman, Nature Physics 14, 542-545 (2018).
13. Message passing on networks with loops, George T. Cantwell and M. E. J. Newman, Proc. Natl. Acad. Sci. USA 116, 23398–23403 (2019).

Research group

• Group members:
  • PI: Professor Mark Newman
  • Max Jerdee
  • Austin Polanco

• Former group members:
  • Dr. Brian Ball, Dotomi Inc.
  • Dr. George Cantwell, Santa Fe Institute
  • Professor Michael Gastner, Yale-NUS College
  • Professor Gourab Ghoshal, University of Rochester
  • Professor Michelle Girvan, University of Maryland
  • Professor Petter Holme, Tokyo Institute of Technology
  • Dr. Brian Karrer, Facebook Corporation
  • Professor Alec Kirkley, City University of Hong Kong
  • Dr. Elizabeth Leicht, Oxford University
  • Dr. Travis Martin, Google Inc.
  • Professor Juyong Park, KAIST, Seoul
  • Professor Bethany Percha, Mount Sinai School of Medicine
  • Dr. Maria Riolo, Santa Fe Institute
  • Professor Jean-Gabriel Young, University of Vermont
  • Dr. Xiao Zhang, Amazon A9 Inc.

Courses

• Physics 288, Physics of Music, Winter 2022

Previous courses:

• Complex Systems 535, Network Theory, Fall 2021
• Physics 288, Physics of Music, Winter 2021
• Complex Systems 535, Network Theory, Fall 2020
• Physics 288, Physics of Music, Winter 2020
• Complex Systems 535, Network Theory, Fall 2019
• Physics 411, Computational Physics, Winter 2019
• Complex Systems 535, Network Theory, Fall 2018
• Physics 411, Computational Physics, Winter 2018
• Complex Systems 535, Network Theory, Fall 2017
• Physics 411, Computational Physics, Winter 2017
• Complex Systems 535, Network Theory, Fall 2015
• Physics 390, Introduction to Modern Physics, Winter 2015
• Complex Systems 535, Network Theory, Fall 2014
• Physics 411, Computational Physics, Winter 2014
• Complex Systems 535, Network Theory, Fall 2013
• Physics 411, Computational Physics, Winter 2013
• Complex Systems 535, Network Theory, Fall 2012
• Physics 411, Computational Physics, Winter 2012
• Complex Systems 535, Network Theory, Fall 2011
• Physics 411, Computational Physics, Winter 2011
• Complex Systems 535, Network Theory, Fall 2010
• Physics 390, Introduction to Modern Physics, Winter 2010
• Complex Systems 535, Network Theory, Fall 2009
• Physics 390, Introduction to Modern Physics, Winter 2008
• Complex Systems 511, Theory of Complex Systems, Fall 2007
• Physics 406, Statistical and Thermal Physics, Winter 2007
• Complex Systems 899, Theory of Complex Systems, Winter 2006
• Complex Systems 535, Network Theory, Winter 2005
• Physics 406, Statistical and Thermal Physics, Fall 2004
• Complex Systems 535, Network Theory, Winter 2004
• Physics 406, Statistical and Thermal Physics, Fall 2003
• Physics 406, Statistical and Thermal Physics, Fall 2002

Other information

The second edition of my book Networks is now available from Oxford University Press. Information about it is here. The Amazon page is also a good place to look for info.

If you are looking for maps of the US election results, click here.

Our work on generalized communities in networks was featured on the cover of Physical Review Letters.

With Daniel Dorling and Anna Barford: The Atlas of the Real World, a popular book containing 366 cartograms showing all kinds of different features of the world today.

Our work on density-equalizing map projections appeared on the cover of the Proceedings of the National Academy of Sciences.

• The web site accompanying my book Computational Physics is here.
• The Worldmapper Project collection of cartograms is here.
• A collection of network data sets from various sources is here. Data sets include the Zachary karate club and US college football networks that have been widely used as tests for community structure algorithms, and a number of coauthorship networks.
• Code for computing representative community divisions of networks is here.
• Code for calculating the reduced mutual information is here.
• Code for calculating the number of communities in a network is here.
• Code for the uncertain networks algorithm is here.
• Code for the metadata community detection algorithm is here.
• Code for the overlapping communities algorithm is here.
• Code for the directed network ranking algorithm is here.
• Information and code for the degree-corrected block model is here.
• Information and code for the link prediction algorithm is here.
• Information and code for maximum likelihood fits to power-law distributions is here.
• Information and code for the fast community structure algorithm is here.
• Information and code for the cartogram algorithm is here.
• Information and code for the percolation algorithm is here.
• If you are looking for the summary tables of collaboration indices from my 2001 paper on coauthorships, they are here.

Contact details:

I am not the only professor called Mark Newman at the University of Michigan. I'm the physicist who works on networks. There is another Mark Newman in the UM School of Information who works on human-computer interaction.

Here are my contact details:

Department of Physics
University of Michigan
Randall Laboratory
450 Church Street
Ann Arbor, MI 48109-1040

Phone: (734) 764-4437
Fax: (734) 764-6843
Email: mejn@umich.edu

Last modified: February 25, 2023