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
Selected publications:
 The structure
of scientific collaboration networks, M. E. J. Newman,
Proc. Natl. Acad. Sci. USA 98, 404409 (2001).
 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).
 Assortative mixing in
networks, M. E. J. Newman, Phys. Rev. Lett. 89, 208701
(2002).
 The structure and
function of complex networks, M. E. J. Newman, SIAM Review
45, 167256 (2003).
 Modularity and
community structure in networks, M. E. J. Newman,
Proc. Natl. Acad. Sci. USA 103, 85778582 (2006).
 Hierarchical structure and the
prediction of missing links in networks, A. Clauset, C. Moore,
and M. E. J. Newman, Nature 453, 98–101 (2008).
 Powerlaw distributions in
empirical data, Aaron Clauset, Cosma Rohilla Shalizi, and
M. E. J. Newman, SIAM Review 51, 661703 (2009).
 Random graphs with
clustering, M. E. J. Newman, Phys. Rev. Lett. 103, 058701
(2009).
 Graph spectra and the
detectability of community structure in networks, Raj Rao
Nadakuditi and M. E. J. Newman, Phys. Rev. Lett. 108, 188701
(2012).
 Percolation on sparse
networks, Brian Karrer, M. E. J. Newman, and Lenka Zdeborová,
Phys. Rev. Lett. 113, 208702 (2014).
 Generalized communities in networks, M. E. J. Newman and Tiago P. Peixoto, Phys. Rev. Lett. 115, 088701 (2015).
 Structure and inference in annotated networks, M. E. J. Newman and Aaron Clauset,
Nature Communications 7, 11863 (2016).
Research group

Group members:
 Former group members:
 Dr. Brian Ball, Dotomi Inc.
 Dr Michael Gastner,
Institute for Technical Physics and Materials Science, Budapest
 Professor Michelle
Girvan, University of Maryland
 Professor Gourab Ghoshal, University of Rochester
 Dr. Petter Holme, Umeå
University
 Dr. Brian Karrer, Facebook Corporation
 Dr Elizabeth Leicht, Oxford University
 Dr. Travis Martin, Google Inc.
 Professor Juyong Park, KAIST, Seoul
 Bethany Percha, Stanford University
Courses
Previous courses:
 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
If you are looking for maps of the US election results, click here.
Our recent work on generalized communities in networks was featured on the cover of
Physical Review Letters.
My book
Networks: An Introduction was published in 2010 by Oxford University
Press. Information about it is here. The Amazon
page is also a good place to look for info.
With Daniel Dorling and Anna
Barford, we have a new book of maps available, The Atlas of the Real
World, containing 366 cartograms showing all kinds of different
features of the world today.
Our work on
densityequalizing 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 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 degreecorrected block model is here.
 Information and code for the link prediction algorithm is here.
 Information and code for maximum likelihood fits to powerlaw
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 humancomputer
interaction.
Here are my contact details:
Department of Physics
University of Michigan
Randall Laboratory
450 Church Street
Ann Arbor, MI 481091040
Phone: (734) 7644437
Fax: (734) 7646843
Email: mejn@umich.edu
Last modified: November 13, 2016
Mark Newman, Department of Physics, University of Michigan