@article{Mucha14052010,
  abstract = {Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.},
  author = {Mucha, Peter J. and Richardson, Thomas and Macon, Kevin and Porter, Mason A. and Onnela, Jukka-Pekka},
  doi = {10.1126/science.1184819},
  eprint = {http://www.sciencemag.org/content/328/5980/876.full.pdf},
  interhash = {7cc01f266e3a745d2be16a9a3b377695},
  intrahash = {c5b7cfb584d5aee1a941a8e5d3e856b1},
  journal = {Science},
  number = 5980,
  pages = {876-878},
  title = {Community Structure in Time-Dependent, Multiscale, and Multiplex Networks},
  url = {http://www.sciencemag.org/content/328/5980/876.abstract},
  volume = 328,
  year = 2010
}

@misc{Traud2011,
  abstract = {  We study the social structure of Facebook "friendship" networks at one
hundred American colleges and universities at a single point in time, and we
examine the roles of user attributes - gender, class year, major, high school,
and residence - at these institutions. We investigate the influence of common
attributes at the dyad level in terms of assortativity coefficients and
regression models. We then examine larger-scale groupings by detecting
communities algorithmically and comparing them to network partitions based on
the user characteristics. We thereby compare the relative importances of
different characteristics at different institutions, finding for example that
common high school is more important to the social organization of large
institutions and that the importance of common major varies significantly
between institutions. Our calculations illustrate how microscopic and
macroscopic perspectives give complementary insights on the social organization
at universities and suggest future studies to investigate such phenomena
further.
},
  author = {Traud, Amanda L. and Mucha, Peter J. and Porter, Mason A.},
  interhash = {2bb0d7d1589f4e651c07f4419bc68c02},
  intrahash = {8afd9e99551c5fc1343fcc47542dbef6},
  note = {cite arxiv:1102.2166
Comment: 82 pages (including many pages of tables), 8 multi-part figures,
  "Facebook100" data used in this paper is publicly available at
  http://people.maths.ox.ac.uk/~porterm/data/facebook100.zip},
  title = {Social Structure of Facebook Networks},
  url = {http://arxiv.org/abs/1102.2166},
  year = 2011
}

@misc{Mucha2009,
  abstract = {  During the last decade, the science of networks has grown into an enormous
interdisciplinary endeavor, with methods and applications drawn from across the
natural, social, and information sciences. One of the most important and
prominent ideas from network science is the algorithmic detection of
tightly-connected groups of nodes known as communities. Here we develop a
formulation to detect communities in a very broad setting by studying general
dynamical processes on networks. We create a new framework of network quality
functions that allows us to study the community structure of arbitrary
multislice networks, which are combinations of individual networks coupled
through additional links that connect each node in one network slice to itself
in other slices. This new framework allows one for the first time to study
community structure in a very general setting that encompasses networks that
evolve in time, have multiple types of ties (multiplexity), and have multiple
scales.
},
  author = {Mucha, Peter J. and Richardson, Thomas and Macon, Kevin and Porter, Mason A. and Onnela, Jukka-Pekka},
  interhash = {1e595ba6a001086edcadb46ccbb41f49},
  intrahash = {e4bdff9d315195bc65a74a5242ad5b19},
  note = {cite arxiv:0911.1824
Comment: 23 pages, 3 figures, 1 table},
  title = {Community Structure in Time-Dependent, Multiscale, and Multiplex
  Networks},
  url = {http://arxiv.org/abs/0911.1824},
  year = 2009
}