@article{white2005sca,
  author = {White, S. and Smyth, P.},
  booktitle = {SIAM Data Mining},
  interhash = {180d37026ab6ea4f4c3f6aba9c405929},
  intrahash = {310763d5fe7195d89883c91c90681e03},
  title = {{A spectral clustering approach to finding communities in graph}},
  year = 2005
}

@article{newman2006mac,
  author = {Newman, MEJ},
  interhash = {e664336d414a1e21d89f30cc56f5e739},
  intrahash = {9104cb1678a39c96b06ed838a8aa3a63},
  journal = {Proceedings of the National Academy of Sciences},
  number = 23,
  pages = {8577--8582},
  publisher = {National Acad Sciences},
  title = {{Modularity and community structure in networks}},
  volume = 103,
  year = 2006
}

@inproceedings{Alpert95spectralpartitioning:,
  author = {Alpert, Charles J. and Kahng, Andrew B. and zen Yao, So},
  booktitle = {Proc. ACM/IEEE Design Automation Conf},
  interhash = {8384ccbb6b91c08c6ce73b2bde86bfd9},
  intrahash = {13da262902e2a4425f7799a519163b99},
  pages = {195--200},
  title = {Spectral partitioning: The more eigenvectors, the better},
  year = 1995
}

@article{New03,
  author = {Newman, M. E. J.},
  interhash = {7bedd01cb4c06af9f5200b0fb3faa571},
  intrahash = {f0de28071b8ee1c3675e67c7538e806a},
  journal = {SIAM Review},
  number = 2,
  pages = {167-256},
  title = {The structure and function of complex networks},
  volume = 45,
  year = 2003
}

@article{citeulike:1025135,
  abstract = {A Family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced. These measures define centrality in terms of the degree to which a point falls on the shortest path between others and therefore has a potential for control of communication. They may be used to index centrality in any large or small network of symmetrical relations, whether connected or unconnected.},
  author = {Freeman, Linton C.},
  citeulike-article-id = {1025135},
  interhash = {7ff13dc1fa197ed99dadeb6e23cf070b},
  intrahash = {05c4b47904e32958af82a2930ef97b3b},
  journal = {Sociometry},
  month = {March},
  number = 1,
  pages = {35--41},
  posted-at = {2007-01-04 16:23:36},
  priority = {2},
  publisher = {American Sociological Association},
  title = {A Set of Measures of Centrality Based on Betweenness},
  url = {http://links.jstor.org/sici?sici=0038-0431\%28197703\%2940\%3A1\%3C35\%3AASOMOC\%3E2.0.CO\%3B2-H},
  volume = 40,
  year = 1977
}

@article{filippone2008ska,
  author = {Filippone, M. and Camastra, F. and Masulli, F. and Rovetta, S.},
  interhash = {3f9fe20110b6e183530cd675bb0ba3e6},
  intrahash = {30fe8946a31d33d0fa81c16ec04287aa},
  journal = {Pattern recognition},
  number = 1,
  pages = {176--190},
  publisher = {Elsevier},
  title = {{A survey of kernel and spectral methods for clustering}},
  volume = 41,
  year = 2008
}

@article{verma2003csc,
  author = {Verma, D. and Meila, M.},
  interhash = {dbf30d8faf00e7187c8e6b620c455b9a},
  intrahash = {829d5543415a8d8cd6c0db75c025c9d3},
  journal = {University of Washington, Tech. Rep. UW-CSE-03-05-01},
  title = {{A comparison of spectral clustering algorithms}},
  year = 2003
}

@article{1288832,
  address = {Hingham, MA, USA},
  author = {Luxburg, Ulrike},
  doi = {http://dx.doi.org/10.1007/s11222-007-9033-z},
  interhash = {ea91cbed60cf8e3dd610a1829afff18f},
  intrahash = {2f579735afb8bbba1da168f1b83e10c7},
  issn = {0960-3174},
  journal = {Statistics and Computing},
  number = 4,
  pages = {395--416},
  publisher = {Kluwer Academic Publishers},
  title = {A tutorial on spectral clustering},
  url = {http://portal.acm.org/citation.cfm?id=1288832},
  volume = 17,
  year = 2007
}

@article{estrada2003smb,
  author = {Estrada, E. and Rodr{\'\i}guez-Vel{\'a}zquez, J.A.},
  interhash = {c010f2da83d3f54d6ca8c4e1a792879c},
  intrahash = {2a2cfdf7b25c7fe86be3ea81aa2324e2},
  journal = {SIAM Rev Phys Rev E},
  pages = 046105,
  publisher = {APS},
  title = {{Spectral measures of bipartivity in complex networks}},
  volume = 72,
  year = 2003
}

@article{mohar1997sal,
  author = {Mohar, B.},
  interhash = {04fbb3036b34072e6d27e7a84cfafb87},
  intrahash = {df49696a4bc6df9a662c95fe8f713beb},
  journal = {Graph Symmetry: Algebraic Methods and Applications},
  pages = {227--275},
  publisher = {Kluwer},
  title = {{Some applications of Laplace eigenvalues of graphs}},
  volume = 497,
  year = 1997
}

@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
}

@techreport{Dhillon:EtAl:05,
  abstract = {Recently, a variety of clustering algorithms have been proposed to handle data that is not linearly separable. Spectral clustering and kernel k-means are two such methods that are seemingly quite different. In this paper, we show that a general weighted kernel k-means objective is mathematically equivalent to a weighted graph partitioning objective. Special cases of this graph partitioning objective include ratio cut, normalized cut and ratio association. Our equivalence has important consequences: the weighted kernel k-means algorithm may be used to directly optimize the graph partitioning objectives, and conversely, spectral methods may be used to optimize the weighted kernel k-means objective. Hence, in cases where eigenvector computation is prohibitive, we eliminate the need for any eigenvector computation for graph partitioning. Moreover, we show that the Kernighan-Lin objective can also be incorporated into our framework, leading to an incremental weighted kernel k-means algorithm for local optim ization of the objective. We further discuss the issue of convergence of weighted kernel k-means for an arbitrary graph affinity matrix and provide a number of experimental results. These results show that non-spectral methods for graph partitioning are as effective as spectral methods and can be used for problems such as image segmentation in addition to data clustering. },
  author = {Dhillon, Inderjit S. and Guan, Yuqiang and Kulis, Brian},
  institution = {University of Texas Dept. of Computer Science},
  interhash = {d4468c8f6dd6ce79177c4be9549bd545},
  intrahash = {21707c7469b7d2d19d388e729b753d91},
  number = {TR-04-25},
  title = {A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts},
  url = {http://www.cs.utexas.edu/ftp/pub/techreports/tr04-25.pdf},
  year = 2005
}

@phdthesis{butler2008eas,
  author = {Butler, S.K.},
  interhash = {b5687ebaaa208e99aa1fda098ab4f1f4},
  intrahash = {5a78466f90155c3d2ea9d6dfb5072c3d},
  school = {University of California, San Diego},
  title = {{Eigenvalues and Structures of Graphs}},
  year = 2008
}

@article{butler:cgb,
  author = {Butler, S.},
  interhash = {f16d0b8f861076987ebb425f10d86702},
  intrahash = {0e4bb8ea8af8a8bf2a02aadddfaa24cd},
  title = {{Cospectral graphs for both the adjacency and normalized Laplacian matrices}},
  year = 2000
}

@unpublished{butler2006-3,
  author = {Butler, Steve},
  interhash = {852cf90cdc865cd9c7985875bcde2160},
  intrahash = {08749179a19f8bc991ed31a5cd75d386},
  title = {Spectral Graph Theory: Cheeger constants and discrepancy},
  year = 2006
}

@unpublished{butler2006,
  author = {Butler, Steve},
  interhash = {21702760185fe285dc7417c3c6710e03},
  intrahash = {cf17d1411eec30ade5414de1342a46d9},
  title = {Spectral Graph Theory: Applications of Courant Fischer},
  year = 2006
}

@unpublished{butler2006-1,
  author = {Butler, Steve},
  interhash = {115d301ff774f0b990c66b354c06f139},
  intrahash = {ed2937203a56179d580680c7bdf9fd9a},
  title = {Spectral Graph Theory: Three common spectra},
  year = 2006
}

@article{guattery1998qss,
  author = {Guattery, S. and Miller, G.L.},
  interhash = {a02ade746e7a052d1162d327c9db1cff},
  intrahash = {2802accf4ca63399a4af7d748f3e3c53},
  journal = {SIAM Journal on Matrix Analysis and Applications},
  number = 3,
  pages = {701--719},
  publisher = {[Philadelphia, Pa.: The Society, c1988-},
  title = {{On the quality of spectral separators}},
  volume = 19,
  year = 1998
}

@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
}