@article{mohar1991lsg,
  author = {Mohar, B.},
  interhash = {571f21e55ceba47912e7729b561be348},
  intrahash = {3d63879e040c1a1ccd239ffaffb0189a},
  journal = {Graph Theory, Combinatorics, and Applications},
  pages = {871--898},
  publisher = {New York: Wiley},
  title = {{The Laplacian spectrum of graphs}},
  volume = 2,
  year = 1991
}

@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{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{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
}

@article{1423975,
  abstract = { This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.},
  author = {Adomavicius, G. and Tuzhilin, A.},
  doi = {10.1109/TKDE.2005.99},
  interhash = {42f7653127a823354d000ea95cf804be},
  intrahash = {da4de5d95427e22d4d1dda8554d19e2f},
  issn = {1041-4347},
  journal = {Knowledge and Data Engineering, IEEE Transactions on},
  month = {June},
  number = 6,
  pages = { 734-749},
  title = {Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions},
  volume = 17,
  year = 2005
}

@article{fassbender2006structured,
  author = {Fassbender, H. and Kressner, D.},
  interhash = {fdb6be4076647bcdaa17acdd12a1f001},
  intrahash = {1b0138899f66fe7fec4a24ecbcefe9d5},
  journal = {GAMM Mitteilungen},
  number = 2,
  pages = {297--318},
  title = {{Structured eigenvalue problems}},
  url = {http://scholar.google.de/scholar.bib?q=info:cGieYDhejsgJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=6},
  volume = 29,
  year = 2006
}

@article{frivolt:cgc,
  author = {Frivolt, G. and Pok, O.},
  interhash = {d6d73db36d0cd29bcb712533d86d06db},
  intrahash = {e7109528877b4ee8fb17acb837740b74},
  title = {{Comparison of Graph Clustering Approaches}},
  year = 2006
}

@proceedings{Gunawardana2935,
  author = {Gunawardana, Asela and Shani, Guy},
  interhash = {331380155bd0e9e72701da97fbd960bf},
  intrahash = {9e89771c4e26a5b4f1f62082c824ca10},
  page = {2935−2962},
  title = { A Survey of Accuracy Evaluation Metrics of Recommendation Tasks },
  url = {http://jmlr.csail.mit.edu/papers/v10/gunawardana09a.html},
  volume = {v10},
  year = 2935
}

@article{Fortunato201075,
  abstract = {The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i.e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e.g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.},
  author = {Fortunato, Santo},
  doi = {DOI: 10.1016/j.physrep.2009.11.002},
  interhash = {9f6089e942903fc65309f77744c88109},
  intrahash = {6901b5b9592c67a121ad6fd297aaa91e},
  issn = {0370-1573},
  journal = {Physics Reports},
  number = {3-5},
  pages = {75 - 174},
  title = {Community detection in graphs},
  url = {http://www.sciencedirect.com/science/article/B6TVP-4XPYXF1-1/2/99061fac6435db4343b2374d26e64ac1},
  volume = 486,
  year = 2010
}

@book{BraErl05,
  author = {Brandes, Ulrik and Erlebach, Thomas},
  interhash = {063c95923f07b0180d16336ef0c8ac56},
  intrahash = {ab8b0742de22b0fa06206089d52883da},
  publisher = {Springer},
  title = {Network Analysis: Methodological Foundations},
  year = 2005
}