@misc{citeulike:155,
  abstract = {Inspired by empirical studies of networked systems such as the Internet,
social networks, and biological networks, researchers have in recent years
developed a variety of techniques and models to help us understand or predict
the behavior of these systems. Here we review developments in this field,
including such concepts as the small-world effect, degree distributions,
clustering, network correlations, random graph models, models of network growth
and preferential attachment, and dynamical processes taking place on networks.},
  author = {Newman, M. E. J.},
  citeulike-article-id = {155},
  eprint = {cond-mat/0303516},
  interhash = {7bedd01cb4c06af9f5200b0fb3faa571},
  intrahash = {d53568209eef08fb0a8734cf34c59a71},
  keywords = {affinity algorithm arch clustering complex-systems folksonomy growth information_retrieval math network network_theory readinggroup scale-free-networks small_world socialnetwork systems thesis web web-graph},
  month = {March},
  priority = {5},
  title = {The structure and function of complex networks},
  url = {http://arxiv.org/abs/cond-mat/0303516},
  year = 2003
}

@inproceedings{schmitz2006content,
  abstract = {Recently, research projects such as PADLR and SWAP
  have developed tools like Edutella or Bibster, which are targeted at
  establishing peer-to-peer knowledge management (P2PKM) systems.  In
  such a system, it is necessary to obtain provide brief semantic
  descriptions of peers, so that routing algorithms or matchmaking
  processes can make decisions about which communities peers should
  belong to, or to which peers a given query should be forwarded.

  This paper provides a graph clustering technique on
  knowledge bases for that purpose. Using this clustering, we can show
  that our strategy requires up to 58% fewer queries than the
  baselines to yield full recall in a bibliographic P2PKM scenario.},
  address = {Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  editor = {Sure, York and Domingue, John},
  interhash = {d2ddbb8f90cd271dc18670e4c940ccfb},
  intrahash = {1788c88e04112a4491f19dfffb8dc39e},
  pages = {530-544},
  publisher = {Springer},
  series = {LNAI},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf},
  volume = 4011,
  year = 2006
}

@inproceedings{schmitz2006content,
  address = {Budva, Montenegro},
  author = {Schmitz, Christoph and Hotho, Andreas and J\"aschke, Robert and Stumme, Gerd},
  booktitle = {Proceedings of the 3rd European Semantic Web Conference},
  interhash = {940fa3c671c771cc9a644b3ecfef43cd},
  intrahash = {9a06428ec3bd72e3ea6c7a8f08e2bb85},
  isbn = {3-540-34544-2},
  month = {June},
  pages = {530-544},
  publisher = {Springer},
  series = {LNCS},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006sumarize_eswc.pdf},
  vgwort = {27},
  volume = 4011,
  year = 2006
}

@article{partitioning89,
  author = {Pothen, A. and Simon, H.D. and Liou, K.P.},
  interhash = {2f6596b5093c7d16e3817dadf39b8aa2},
  intrahash = {7444914fc73fc8ec12a67e5e172c34c0},
  journal = {SIAM J. MATRIX ANAL. APPLIC.},
  number = 3,
  pages = {430--452},
  title = {{Partitioning Sparse Matrices with Eigenvectors of Graphs}},
  url = {http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19970011963_1997016998.pdf },
  volume = 11,
  year = 1990
}

@unpublished{ranade:sus,
  author = {Ranade, A.G.},
  interhash = {473e92f688426631dd9ccc7639a5e861},
  intrahash = {9f7f1562631792a85f2dd5f5eefbcf4d},
  title = {{Some uses of spectral methods}},
  year = 2000
}

@techreport{Spielman:1996,
  address = {Berkeley, CA, USA},
  author = {Spielman, Daniel A. and Teng, Shang},
  interhash = {83f3d15605beda920551830ccac3d79a},
  intrahash = {06b1b19e0a29a145555cb1526716c451},
  publisher = {University of California at Berkeley},
  title = {Spectral Partitioning Works: Planar Graphs and Finite Element Meshes},
  year = 1996
}

@article{donath1973lbp,
  author = {Donath, W.E. and Hoffman, A.J.},
  interhash = {ff38bdeb46caa114a3efad739319973f},
  intrahash = {7cb789bd22dfa8ccdd2abdd30121dfc9},
  journal = {IBM Journal of Research and Development},
  number = 5,
  pages = {420--425},
  title = {{Lower bounds for the partitioning of graphs}},
  volume = 17,
  year = 1973
}

@inproceedings{Ng01onspectral,
  abstract = {Despite many empirical successes of spectral clustering methods| algorithms that cluster points using eigenvectors of matrices derived from the data|there are several unresolved issues. First, there are a wide variety of algorithms that use the eigenvectors in slightly dierent ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results on a number of challenging clustering problems. 1},
  author = {Ng, Andrew Y. and Jordan, Michael I. and Weiss, Yair},
  booktitle = {Advances in Neural Information Processing Systems 14},
  interhash = {b72c97e659127fc653a0d51143d85b0c},
  intrahash = {7485849e42418ee5ceefb45dc6eb603c},
  pages = {849--856},
  publisher = {MIT Press},
  title = {On spectral clustering: Analysis and an algorithm},
  year = 2001
}

@inproceedings{dhillon:mallela:modha:03,
  author = {Dhillon, I. S. and Mallela, S. and Modha, D. S.},
  booktitle = {Proceedings of The Ninth ACM SIGKDD International              Conference on Knowledge Discovery and Data Mining({KDD}-2003)},
  interhash = {30fe4c22011ee3c5565d35709d9ce1f1},
  intrahash = {f7d4e60faba05f7c41e6836a8fbee5b2},
  pages = {89--98},
  title = {Information-Theoretic Co-Clustering},
  url = {/brokenurl#citeseer.ist.psu.edu/dhillon03informationtheoretic.html},
  year = 2003
}

@inproceedings{schmitz2006content,
  abstract = {Recently, research projects such as PADLR and SWAP
  have developed tools like Edutella or Bibster, which are targeted at
  establishing peer-to-peer knowledge management (P2PKM) systems.  In
  such a system, it is necessary to obtain provide brief semantic
  descriptions of peers, so that routing algorithms or matchmaking
  processes can make decisions about which communities peers should
  belong to, or to which peers a given query should be forwarded.

  This paper provides a graph clustering technique on
  knowledge bases for that purpose. Using this clustering, we can show
  that our strategy requires up to 58% fewer queries than the
  baselines to yield full recall in a bibliographic P2PKM scenario.},
  address = {Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  editor = {Sure, York and Domingue, John},
  interhash = {d2ddbb8f90cd271dc18670e4c940ccfb},
  intrahash = {1788c88e04112a4491f19dfffb8dc39e},
  pages = {530-544},
  publisher = {Springer},
  series = {LNAI},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf},
  volume = 4011,
  year = 2006
}

@incollection{springerlink:10.1007/978-3-540-74839-7_12,
  abstract = {Modularity is a recently introduced quality measure for graph clusterings. It has immediately received considerable attention in several disciplines, and in particular in the complex systems literature, although its properties are not well understood. We study the problem of finding clusterings with maximum modularity, thus providing theoretical foundations for past and present work based on this measure. More precisely, we prove the conjectured hardness of maximizing modularity both in the general case and with the restriction to cuts, and give an Integer Linear Programming formulation. This is complemented by first insights into the behavior and performance of the commonly applied greedy agglomaration approach.},
  address = {Berlin / Heidelberg},
  affiliation = {Department of Computer and Information Science, University of Konstanz},
  author = {Brandes, Ulrik and Delling, Daniel and Gaertler, Marco and Görke, Robert and Hoefer, Martin and Nikoloski, Zoran and Wagner, Dorothea},
  booktitle = {Graph-Theoretic Concepts in Computer Science},
  doi = {10.1007/978-3-540-74839-7_12},
  editor = {Brandstädt, Andreas and Kratsch, Dieter and Müller, Haiko},
  interhash = {b335302041d1865d7cfec7467e8e2999},
  intrahash = {6fd10991ee4e3880c64c11862884ead7},
  isbn = {978-3-540-74838-0},
  keyword = {Computer Science},
  openurl = {http://www.blub.de},
  pages = {121-132},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {On Finding Graph Clusterings with Maximum Modularity},
  url = {http://dx.doi.org/10.1007/978-3-540-74839-7_12},
  volume = 4769,
  year = 2007
}