@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
}

@article{karypis1997mhp,
  author = {Karypis, G. and Aggarwal, R. and Kumar, V. and Shekhar, S.},
  booktitle = {Proceedings of the 34th annual conference on Design automation},
  interhash = {77aa28416ddb1c22834224cf0b09c74e},
  intrahash = {bdfb6003e7a8786b0a5649bb250c0a77},
  organization = {ACM New York, NY, USA},
  pages = {526--529},
  title = {{Multilevel hypergraph partitioning: Application in VLSI domain}},
  year = 1997
}

@inproceedings{Karypis98multilevelk-way,
  abstract = {In this paper, we present a new multilevel k-way hypergraph partitioning algorithm that substantially outperforms the existing state-of-the-art K-PM/LR algorithm for multi-way partitioning. both for optimizing local as well as global objectives. Experiments on the ISPD98 benchmark suite show that the partitionings produced by our scheme are on the average 15% to 23% better than those produced by the K-PM/LR algorithm, both in terms of the hyperedge cut as well as the (K  -  1) metric. Furthermore, our algorithm is significantly faster, requiring 4 to 5 times less time than that required by K-PM/LR. 1 Introduction  Hypergraph partitioning is an important problem with extensive application to many areas, including VLSI design [10], efficient storage of large databases on disks [14], and data mining [13]. The problem is to partition the vertices of a hypergraph into k roughly equal parts, such that a certain objective function defined over the hyperedges is optimized. A commonly used obje...},
  author = {Karypis, George and Kumar, Vipin},
  booktitle = {In Proceedings of the Design and Automation Conference},
  interhash = {413d89f472133bf5ff0671cccc818f55},
  intrahash = {d63a73732f65ce10595e210cedda3bd1},
  pages = {343--348},
  title = {Multilevel k-way Hypergraph Partitioning},
  year = 1998
}

@article{brandes2003experiments,
  author = {Brandes, U. and Gaertler, M. and Wagner, D.},
  interhash = {b527c5ab05bac6d10b7768c08fdf7860},
  intrahash = {191613112620e6261271504e5cf992e1},
  journal = {Lecture notes in computer science},
  pages = {568--579},
  publisher = {Springer},
  title = {{Experiments on graph clustering algorithms}},
  url = {http://scholar.google.de/scholar.bib?q=info:gDNQfOoSm6cJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=2},
  year = 2003
}

@article{flake2004graph,
  author = {Flake, G.W. and Tarjan, R.E. and Tsioutsiouliklis, K.},
  interhash = {c6e80a1ce6ca8013e94aa67a27e1fa92},
  intrahash = {6cb5211e40c6ff08605f69b133326a0b},
  journal = {Internet Mathematics},
  number = 4,
  pages = {385--408},
  publisher = {AK Peters},
  title = {{Graph clustering and minimum cut trees}},
  url = {http://scholar.google.de/scholar.bib?q=info:27hvFfjDdrkJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=6},
  volume = 1,
  year = 2004
}

@article{cantador:bes,
  author = {Cantador, I. and Castells, P.},
  interhash = {876722af69956aa42f7cb1260b456a5a},
  intrahash = {eeb2ea8c8f39f6057465b38eea991582},
  title = {{Building Emergent Social Networks and Group Profiles by Semantic User Preference Clustering}},
  year = 2006
}

@article{schaeffer2007graph,
  author = {Schaeffer, S.E.},
  interhash = {24c764dba3c31f76ced3aa58f1983ed4},
  intrahash = {d16e5e8575fcb716101fcd2762f2a2a1},
  journal = {Computer Science Review},
  number = 1,
  pages = {27--64},
  publisher = {Elsevier},
  title = {{Graph clustering}},
  url = {http://scholar.google.de/scholar.bib?q=info:-vQhplU2EFYJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0},
  volume = 1,
  year = 2007
}

@article{barber2007mac,
  abstract = {The modularity of a network quantifies the extent, relative to a null model network, to which vertices cluster into community groups. We define a null model appropriate for bipartite networks, and use it to define a bipartite modularity. The bipartite modularity is presented in terms of a modularity matrix B; some key properties of the eigenspectrum of B are identified and used to describe an algorithm for identifying modules in bipartite networks. The algorithm is based on the idea that the modules in the two parts of the network are dependent, with each part mutually being used to induce the vertices for the other part into the modules. We apply the algorithm to real-world network data, showing that the algorithm successfully identifies the modular structure of bipartite networks.},
  author = {Barber, M. J.},
  doi = {10.1103/PhysRevE.76.066102},
  interhash = {e1d9f528c49b34ff4a05b2b0060bd653},
  intrahash = {61f9d5839845d5d8fa1883a46a2f7744},
  journal = {Physical Review E},
  number = 6,
  title = {Modularity and community detection in bipartite networks},
  url = {http://arxiv.org/abs/arXiv:0707.1616},
  volume = 76,
  year = 2007
}

@article{guimera2007mib,
  abstract = {Modularity is one of the most prominent properties of real-world complex networks. Here, we address the issue of module identification in two important classes of networks: bipartite networks and directed unipartite networks. Nodes in bipartite networks are divided into two non-overlapping sets, and the links must have one end node from each set. Directed unipartite networks only have one type of nodes, but links have an origin and an end. We show that directed unipartite networks can be conviniently represented as bipartite networks for module identification purposes. We report a novel approach especially suited for module detection in bipartite networks, and define a set of random networks that enable us to validate the new approach.},
  author = {Guimer{\`a}, R. and Sales-Pardo, M. and Amaral, L.A.N.},
  doi = {10.1103/PhysRevE.76.036102},
  interhash = {a87821c7c8e7d5ca89cb369e6215a0f3},
  intrahash = {6145a42fe04aee556fa7a68c7cea7db3},
  journal = {Physical review. E, Statistical, nonlinear, and soft matter physics},
  number = {3 Pt 2},
  pages = 036102,
  publisher = {NIH Public Access},
  title = {Module identification in bipartite and directed networks},
  url = {http://arxiv.org/abs/physics/0701151},
  volume = 76,
  year = 2007
}

@inproceedings{1281269,
  address = {New York, NY, USA},
  author = {Tantipathananandh, Chayant and Berger-Wolf, Tanya and Kempe, David},
  booktitle = {KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {http://doi.acm.org/10.1145/1281192.1281269},
  interhash = {9373b48866b4faa1941db0bee9265af0},
  intrahash = {27a4fb58300979d4dbe94e75422418bd},
  isbn = {978-1-59593-609-7},
  location = {San Jose, California, USA},
  pages = {717--726},
  publisher = {ACM},
  title = {A framework for community identification in dynamic social networks},
  url = {http://portal.acm.org/citation.cfm?doid=1281192.1281269},
  year = 2007
}