@inproceedings{zesch2007analysis,
  abstract = {In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graph-theoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms. },
  address = {Rochester},
  author = {Zesch, Torsten and Gurevych, Iryna},
  booktitle = {Proceedings of the TextGraphs-2 Workshop (NAACL-HLT)},
  interhash = {0401e62edb9bfa85dd498cb40301c0cb},
  intrahash = {332ed720a72bf069275f93485432314b},
  month = apr,
  pages = {1--8},
  publisher = {Association for Computational Linguistics},
  title = {Analysis of the Wikipedia Category Graph for NLP Applications},
  url = {http://acl.ldc.upenn.edu/W/W07/W07-02.pdf#page=11},
  year = 2007
}

@article{gansner2009drawing,
  abstract = {Information visualization is essential in making sense out of large data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through  dimensionality reduction techniques. However, these traditional methods often do not capture well the underlying structural information, clustering, and neighborhoods. In this paper, we describe GMap: a practical tool for visualizing relational data with geographic-like maps. We illustrate the effectiveness of this approach with examples from several domains All the maps referenced in this paper can be found in http://www.research.att.com/~yifanhu/GMap },
  author = {Gansner, Emden R. and Hu, Yifan and Kobourov, Stephen G.},
  interhash = {881280a1a2aa34d84322d3781f62ca90},
  intrahash = {3f9e522da9443c0a07c39009918a4a77},
  journal = {cs.CG},
  month = jul,
  title = {{GMap}: Drawing Graphs as Maps},
  url = {http://arxiv.org/abs/0907.2585},
  volume = {arXiv:0907.2585v1},
  year = 2009
}

@article{journals/corr/abs-1006-1260,
  author = {Isella, Lorenzo and Stehlé, Juliette and Barrat, Alain and Cattuto, Ciro and Pinton, Jean-François and den Broeck, Wouter Van},
  ee = {http://arxiv.org/abs/1006.1260},
  interhash = {4a20da6d41e4c1e86e8c04c47b22237c},
  intrahash = {53c0555c19fbfd6af5952e2a3abcbdd2},
  journal = {CoRR},
  note = {informal publication},
  title = {What's in a crowd? Analysis of face-to-face behavioral networks},
  url = {http://dblp.uni-trier.de/db/journals/corr/corr1006.html#abs-1006-1260},
  volume = {abs/1006.1260},
  year = 2010
}

@inproceedings{conf/hipc/HarishN07,
  author = {Harish, Pawan and Narayanan, P. J.},
  booktitle = {HiPC},
  crossref = {conf/hipc/2007},
  date = {2008-01-25},
  editor = {Aluru, Srinivas and Parashar, Manish and Badrinath, Ramamurthy and Prasanna, Viktor K.},
  ee = {http://dx.doi.org/10.1007/978-3-540-77220-0_21},
  interhash = {49830572447ff1f5fe6dd6c3879725ba},
  intrahash = {00821d3bcac5f90eb2e6b90b17464037},
  isbn = {978-3-540-77219-4},
  pages = {197-208},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Accelerating Large Graph Algorithms on the GPU Using CUDA.},
  url = {http://dblp.uni-trier.de/db/conf/hipc/hipc2007.html#HarishN07},
  volume = 4873,
  year = 2007
}

@book{nooy2005pajek,
  address = {New York, NY, USA},
  asin = {0521602629},
  author = {de Nooy, Wouter and Mrvar, Andrej and Batagelj, Vladimir},
  dewey = {300.285},
  ean = {9780521602624},
  interhash = {deb32e4829c9c2b16857a7ced06b89eb},
  intrahash = {114a68aea38d947757b10531d599e6b8},
  isbn = {0521602629},
  number = 27,
  publisher = {Cambridge University Press},
  series = {Structural Analysis in the Social Sciences},
  title = {Exploratory Social Network Analysis with Pajek},
  url = {http://www.amazon.com/Exploratory-Network-Analysis-Structural-Sciences/dp/0521602629%3FSubscriptionId%3D192BW6DQ43CK9FN0ZGG2%26tag%3Dws%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D0521602629},
  year = 2005
}

@article{newman2002assortative,
  abstract = {A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.},
  author = {Newman, M. E. J.},
  doi = {10.1103/PhysRevLett.89.208701},
  interhash = {7265c6dc287861591f52e46b17404a08},
  intrahash = {3ba2913f29e817d122b41e8d78aeeecf},
  journal = {Physical Review Letters},
  month = oct,
  number = 20,
  pages = 208701,
  publisher = {American Physical Society},
  title = {Assortative Mixing in Networks},
  url = {http://link.aps.org/doi/10.1103/PhysRevLett.89.208701},
  volume = 89,
  year = 2002
}

@phdthesis{augeri2008graph,
  abstract = {Graphs express relationships among objects, such as the radio connectivity among nodes in unmanned vehicle swarms. Some applications may rank a swarm's nodes by their relative importance, for example, using the PageRank algorithm applied in certain search engines to order query responses. The PageRank values of the nodes correspond to a unique eigenvector that can be computed using the power method, an iterative technique based on matrix multiplication. The first result is a practical lower bound on the PageRank algorithm's execution time that is derived by applying assumptions to the PageRank perturbation's scaling value and the PageRank vector's required numerical precision. The second result establishes nodes contained in the same block of the graph's coarsest equitable partition must have equal PageRank values. The third result, the AverageRank algorithm, ensures such nodes are assigned equal PageRank values. The fourth result, the ProductRank algorithm, reduces the time needed to find the PageRank vector by eliminating certain dot products in the power method if the graph's coarsest equitable partition contains blocks composed of multiple vertices. The fifth result, the QuotientRank algorithm, uses a quotient matrix induced by the coarsest equitable partition to further reduce the time needed to compute a swarm's PageRank vector.},
  address = {Wright-Patterson Air Force Base, Ohio},
  author = {Augeri, Christopher J.},
  interhash = {ae4510331651ba7525daa04479a065ca},
  intrahash = {af40ef13e09f4dda128456130bd491de},
  month = {September},
  school = {Air Force Institute of Technology},
  title = {On Graph Isomorphism and the PageRank Algorithm},
  url = {http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA490530},
  year = 2008
}

@article{freeman1993galois,
  abstract = {Galois lattices are introduced as a device to provide a general representation for two mode social network data. It is shown that Galois lattices yield a single visual image of such data in cases where most alternative models produce dual images. The inzage provided by the Galois lattice produces, moreover, an inzage that can suggest useful insights about the structural properties of the data. An example, based on data from Davis, Gardner, and Gardner (1941), is used to spell out in detail the kinds of structural insights that can be gained from this approach. In addition, other potential applications are suggested.},
  author = {Freeman, L.C. and White, D.R.},
  interhash = {8231848d3051b517f6dc33e54e6e76d2},
  intrahash = {50103469c4e839b6f05a522eaacaa3a8},
  journal = {Sociological Methodology},
  pages = {127--146},
  title = {Using Galois Lattices to Represent Network Data},
  url = {http://www.polisci.berkeley.edu/courses/coursepages/Fall2004/ps289/Galois.pdf},
  volume = 23,
  year = 1993
}

@article{batagelj2002generalized,
  abstract = {Cores are, besides connectivity components, one among few concepts that provides us with efficient decompositions of large graphs and networks. In the paper a generalization of the notion of core of a graph based on vertex property function is presented. It is shown that for the local monotone vertex property functions the corresponding cores can be determined in $O(m \max (\Delta, \log n))$ time.},
  author = {Batagelj, V. and Zaversnik, M.},
  interhash = {775d7337332536953aaac48aedae1a68},
  intrahash = {9a2144b87c422fae12b4cf1fe2613399},
  journal = {CoRR},
  title = {Generalized Cores},
  url = {http://arxiv.org/abs/cs/0202039},
  volume = {cs.DS/0202039},
  year = 2002
}

@article{dorogovtsev-2006-96,
  author = {Dorogovtsev, S. N. and Goltsev, A. V. and Mendes, J. F. F.},
  interhash = {5cfc560065a5d2be9c0dfa194826218f},
  intrahash = {da319f745eb44dfd197ccddab3384024},
  journal = {Physical Review Letters},
  pages = 040601,
  title = {k-core organization of complex networks},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0509102},
  volume = 96,
  year = 2006
}

@misc{alvarezhamelin-2005,
  author = {Alvarez-Hamelin, Jose Ignacio and Dall'Asta, Luca and Barrat, Alain and Vespignani, Alessandro},
  interhash = {f59a7aa8620977a2ca58e75ae5a03930},
  intrahash = {ea1566a1e88a30950615c7d660a9eb6f},
  title = {k-core decomposition: a tool for the analysis of large scale Internet  graphs},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0511007},
  year = 2005
}