@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{Ghosh2009,
  abstract = {  Heterogeneous networks play a key role in the evolution of communities and
the decisions individuals make. These networks link different types of
entities, for example, people and the events they attend. Network analysis
algorithms usually project such networks unto simple graphs composed of
entities of a single type. In the process, they conflate relations between
entities of different types and loose important structural information. We
develop a mathematical framework that can be used to compactly represent and
analyze heterogeneous networks that combine multiple entity and link types. We
generalize Bonacich centrality, which measures connectivity between nodes by
the number of paths between them, to heterogeneous networks and use this
measure to study network structure. Specifically, we extend the popular
modularity-maximization method for community detection to use this centrality
metric. We also rank nodes based on their connectivity to other nodes. One
advantage of this centrality metric is that it has a tunable parameter we can
use to set the length scale of interactions. By studying how rankings change
with this parameter allows us to identify important nodes in the network. We
apply the proposed method to analyze the structure of several heterogeneous
networks. We show that exploiting additional sources of evidence corresponding
to links between, as well as among, different entity types yields new insights
into network structure.
},
  author = {Ghosh, Rumi and Lerman, Kristina},
  interhash = {761e199eb96643cf601e15cb03c3285a},
  intrahash = {3a4a889123d20e0a4d14d06f670de54b},
  note = {cite arxiv:0906.2212
},
  title = {Structure of Heterogeneous Networks},
  url = {http://arxiv.org/abs/0906.2212},
  year = 2009
}