@article{kolda2009tensor,
  abstract = {This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.},
  author = {Kolda, Tamara G. and Bader, Brett W.},
  doi = {10.1137/07070111X},
  interhash = {b30bb2d42e1a05fc41370c50844822ad},
  intrahash = {e52e5c7bff59fd01fb6497d3bb620077},
  issn = {00361445},
  journal = {SIAM Review},
  number = 3,
  pages = {455--500},
  publisher = {SIAM},
  title = {Tensor Decompositions and Applications},
  url = {http://dx.doi.org/10.1137/07070111X},
  volume = 51,
  year = 2009
}

@inproceedings{kdml2,
  abstract = {The discovery of communities or interrelations in social networks has become an important area of research. The increasing amount of information available in these networks and its decreasing life-time poses tight constraints on the information processing – storage of the data is often prohibited due to its sheer volume. In this paper we adapt a flexible approach for community discovery offering the integration of new information into the model. The continuous integration is combined with a time-based weighting of the data allowing for disposing obsolete information from the model building process. We demonstrate the usefulness of our approach by applying it on the popular Twitter network. The proposed solution can be directly fed with streaming data from Twitter, providing an up-todate community model.},
  address = {Kassel, Germany},
  author = {Bockermann, Christian and Jungermann., Felix},
  booktitle = {Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen {\&} Adaptivitaet},
  crossref = {lwa2010},
  editor = {Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  interhash = {25e36bf796df1bd4c22a0a4b0c2d60cf},
  intrahash = {e0e77b218030558331e985746e824da0},
  presentation_end = {2010-10-05 16:22:30},
  presentation_start = {2010-10-05 16:00:00},
  room = {0446},
  session = {kdml2},
  title = {Stream-based Community Discovery via Relational Hypergraph Factorization on Evolving Networks},
  track = {kdml},
  url = {http://www.kde.cs.uni-kassel.de/conf/lwa10/papers/kdml2.pdf},
  year = 2010
}

@inproceedings{WaAh04,
  author = {Wang, Hongcheng and Ahuja, Narendra},
  booktitle = {ICPR (1)},
  ee = {http://csdl.computer.org/comp/proceedings/icpr/2004/2128/01/212810044abs.htm},
  interhash = {8e1acfd0bb4bb34d6bf26bd9a476019b},
  intrahash = {b6ca9a81389eef7bc3c5d8c7141ec8db},
  pages = {44-47},
  title = {Compact Representation of Multidimensional Data Using Tensor Rank-One Decomposition.},
  url = {http://vision.ai.uiuc.edu/~wanghc/papers/icpr04_tensor.pdf},
  year = 2004
}