@inproceedings{conf/kdd/ChiSZHT07,
  author = {Chi, Yun and Song, Xiaodan and Zhou, Dengyong and Hino, Koji and Tseng, Belle L.},
  booktitle = {KDD},
  crossref = {conf/kdd/2007},
  date = {2007-08-23},
  editor = {Berkhin, Pavel and Caruana, Rich and Wu, Xindong},
  ee = {http://doi.acm.org/10.1145/1281192.1281212},
  interhash = {542ce3968b0d75048000f35669a7fb83},
  intrahash = {0829ef077986e88540a96bd8ba154d86},
  isbn = {978-1-59593-609-7},
  pages = {153-162},
  publisher = {ACM},
  title = {Evolutionary spectral clustering by incorporating temporal smoothness.},
  url = {http://dblp.uni-trier.de/db/conf/kdd/kdd2007.html#ChiSZHT07},
  year = 2007
}

@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
}

@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{Approximating2008Java,
  abstract = {In many social media applications, a small fraction of the members are highly linked while most are sparsely connected to the network. Such a skewed distribution is sometimes referred to as the"long tail". Popular applications like meme trackers and content aggregators mine for information from only the popular blogs located at the head of this curve. On the other hand, the long tail contains large volumes of interesting information and niches. The question we address in this work is how best to approximate the community membership of entities in the long tail using only a small percentage of the entire graph structure. Our technique utilizes basic linear algebra manipulations and spectral methods. It has the advantage of quickly and efficiently finding a reasonable approximation of the community structure of the overall network. Such a method has significant applications in blog analysis engines as well as social media monitoring tools in general. },
  author = {Java, Akshay and Joshi, Anupam and FininBook, Tim},
  booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)},
  date = {2008 Abstract:},
  interhash = {ede357e110fee8803dc181d262f30087},
  intrahash = {386f36679c111f30e37ced272d5b355c},
  publisher = {AAAI Press},
  title = {Approximating the Community Structure of the Long Tail},
  url = {http://ebiquity.umbc.edu/paper/html/id/381/Approximating-the-Community-Structure-of-the-Long-Tail},
  year = 2008
}

@article{Karamolegkos20091498,
  abstract = {In this paper, we provide the results of ongoing work in Magnet Beyond project, regarding social networking services. We introduce an integrated social networking framework through the definition or the appropriate notions and metrics. This allows one to run an evaluation study of three widely used clustering methods (k-means, hierarchical and spectral clustering) in the scope of social groups assessment and in regard to the cardinality of the profile used to assess users' preferences. Such an evaluation study is performed in the context of our service requirements (i.e. on the basis of equal-sized group formation and of maximization of interests' commonalities between users within each social group). The experimental results indicate that spectral clustering, due to the optimization it offers in terms of normalized cut minimization, is applicable within the context of Magnet Beyond socialization services. Regarding profile's cardinality impact on the system performance, this is shown to be highly dependent on the underlying distribution that characterizes the frequency of user preferences appearance. Our work also incorporates the introduction of a heuristic algorithm that assigns new users that join the service into appropriate social groups, once the service has been initialized and the groups have been assessed using spectral clustering. The results clearly show that our approach is able to adhere to the service requirements as new users join the system, without the need of an iterative spectral clustering application that is computationally demanding.},
  author = {Karamolegkos, Pantelis N. and Patrikakis, Charalampos Z. and Doulamis, Nikolaos D. and Vlacheas, Panagiotis T. and Nikolakopoulos, Ioannis G.},
  doi = {DOI: 10.1016/j.camwa.2009.05.023},
  interhash = {e552c077fe0d564429c46a10333bd944},
  intrahash = {d054b1119e1c0c5d8f91588a9e6aca1f},
  issn = {0898-1221},
  journal = {Computers & Mathematics with Applications},
  number = 8,
  pages = {1498--1519},
  title = {An evaluation study of clustering algorithms in the scope of user communities assessment},
  url = {http://www.sciencedirect.com/science/article/B6TYJ-4X076YS-2/2/7afea12ef4d18d39c6efe70be76aa201},
  volume = 58,
  year = 2009
}

@article{Jiang20091479,
  abstract = {Exploring recent developments in spectral clustering, we discovered that relaxing a spectral reformulation of Newman's Q-measure (a measure that may guide the search for-and help to evaluate the fit of - community structures in networks) yields a new framework for use in detecting fuzzy communities and identifying so-called unstable nodes. In this note, we present and illustrate this approach, which we expect to further enhance our understanding of the intrinsic structure of networks and of network-based clustering procedures. We applied a variation of the fuzzy k-means algorithm, an instance of our framework, to two social networks. The computational results illustrate its potential.},
  author = {Jiang, Jeffrey Q. and Dress, Andreas W.M. and Yang, Genke},
  doi = {10.1016/j.aml.2009.02.005},
  interhash = {08fe9886403ff8d2564fca447aef8172},
  intrahash = {d9a603d42a7379d13d8a04404bb951cc},
  issn = {0893-9659},
  journal = {Applied Mathematics Letters},
  number = 9,
  pages = {1479 - 1482},
  title = {A spectral clustering-based framework for detecting community structures in complex networks},
  url = {http://www.sciencedirect.com/science/article/B6TY9-4W6XYH5-5/2/693a9ed19784792496c83e96b4fa828b},
  volume = 22,
  year = 2009
}