@article{herlocker2004ecf,
  author = {Herlocker, J.L. and Konstan, J.A. and Terveen, L.G. and Riedl, J.T.},
  interhash = {f8a70731d983634ac7105896d101c9d2},
  intrahash = {2ed91ee4c94d0a30e3f77dde9de36f66},
  journal = {ACM Transactions on Information Systems},
  number = 1,
  pages = {5--53},
  title = {{Evaluating collaborative filtering recommender systems}},
  volume = 22,
  year = 2004
}

@article{mikel2005storage,
  abstract = {Many storage formats (or data structures) have been proposed to represent sparse matrices. This paper presents a performance evaluation in Java comparing eight of the most popular formats plus one recently proposed specifically for Java (by Gundersen and Steihaug [6] – Java Sparse Array) using the matrix-vector multiplication operation.
ER  -},
  author = {Luján, Mikel and Usman, Anila and Hardie, Patrick and Freeman, T.L. and Gurd, John},
  interhash = {b8bf03d4eba50d021e57b044cf7bb743},
  intrahash = {ba051dc3799456ac8b6ae74b75f7e54b},
  journal = {Computational Science – ICCS 2005},
  pages = {364--371},
  title = {Storage Formats for Sparse Matrices in Java},
  url = {http://dx.doi.org/10.1007/11428831_45},
  year = 2005
}

@article{hubert1985comparing,
  author = {Hubert, L. and Arabie, P.},
  interhash = {c9272c8f9d5aff7ebe43325b5b36ba39},
  intrahash = {4a3c884446b9c874b40588f9d86c0f6c},
  journal = {Journal of classification},
  number = 1,
  pages = {193--218},
  publisher = {Springer},
  title = {{Comparing partitions}},
  url = {http://scholar.google.de/scholar.bib?q=info:IkrWWF2JxwoJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0},
  volume = 2,
  year = 1985
}

@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{markus2003cocitation,
  abstract = {Abstract  After 30 years of research, co-citation analysis has become the dominant method for the empirical study of the structures
of scientific communication. There is a considerable variety of methods and, at the same time, a lack of methodological evaluation.This paper summarizes the present state of co-citation analysis and presents several methods of clustering references. Thedatabase used is a selection of 2,114 documents in the field of organization studies from 1986-2000. The evaluative studyshows that the choice of methods has a strong impact on the results created. It also shows that the methods of cluster andfactor analysis hitherto used have only a limited value in differentiating clearly between schools of scientific research- the 'invisible colleges'.},
  author = {Gmür, Markus},
  interhash = {f8d7adc52a63ff593c04f64251c9dffa},
  intrahash = {f5540ba3281db06a31c1bf73db95be44},
  journal = {Scientometrics},
  month = {#may#},
  number = 1,
  pages = {27--57},
  title = {Co-citation analysis and the search for invisible colleges: A methodological evaluation},
  url = {http://dx.doi.org/10.1023/A:1023619503005},
  volume = 57,
  year = 2003
}

@article{rand1971,
  author = {Rand, W.M.},
  interhash = {1afaf0170bc705a9e49b625f67679ee2},
  intrahash = {fd52548cb4bcd8e83dd27e4b55eff1f3},
  journal = {Journal of the American Statistical Association },
  number = 336,
  pages = {846-850},
  title = {Objective criteria for the evaluation of clustering methods},
  volume = 66,
  year = 1971
}

@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
}

@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
}

@misc{Leskovec2010,
  abstract = {  Detecting clusters or communities in large real-world graphs such as large
social or information networks is a problem of considerable interest. In
practice, one typically chooses an objective function that captures the
intuition of a network cluster as set of nodes with better internal
connectivity than external connectivity, and then one applies approximation
algorithms or heuristics to extract sets of nodes that are related to the
objective function and that "look like" good communities for the application of
interest. In this paper, we explore a range of network community detection
methods in order to compare them and to understand their relative performance
and the systematic biases in the clusters they identify. We evaluate several
common objective functions that are used to formalize the notion of a network
community, and we examine several different classes of approximation algorithms
that aim to optimize such objective functions. In addition, rather than simply
fixing an objective and asking for an approximation to the best cluster of any
size, we consider a size-resolved version of the optimization problem.
Considering community quality as a function of its size provides a much finer
lens with which to examine community detection algorithms, since objective
functions and approximation algorithms often have non-obvious size-dependent
behavior.
},
  author = {Leskovec, Jure and Lang, Kevin J. and Mahoney, Michael W.},
  interhash = {0e58de655596b2198f4a7001facd0c32},
  intrahash = {410a9cbea51ea5dd3c56aad26a0e11b2},
  note = {cite arxiv:1004.3539
},
  title = {Empirical Comparison of Algorithms for Network Community Detection},
  url = {http://arxiv.org/abs/1004.3539},
  year = 2010
}