@article{pennock2002winners,
  author = {Pennock, David and Flake, Gary and Lawrence, Steve and Glover, Eric and Giles, C. Lee},
  interhash = {1a0fa8a805c65f5a4096627c1e019da4},
  intrahash = {10554994432471894ca93bd8a0493e17},
  journal = {Proc.\ National Academy of Sciences},
  misc = {comment = {Lokal vorhanden; PLOD-Algorithmus -> Faloutsos}},
  month = {April},
  number = 8,
  pages = {5207--5211},
  title = {Winners don't take all: Characterizing the
                  competition for links on the web},
  volume = 99,
  year = 2002
}

@article{newman2001rga,
  author = {Newman, MEJ and Strogatz, SH and Watts, DJ},
  interhash = {706d572ebbb2408b5a4ffa6978579dec},
  intrahash = {08a607a8657ec747029ecbaf8d9f224f},
  journal = {Arxiv preprint cond-mat/0007235},
  title = {{Random graphs with arbitrary degree distributions and their applications}},
  year = 2001
}

@misc{molloy_reed95,
  author = {Molloy, M. and Reed, B.},
  interhash = {0998c00ecea7c5a7ea384898aa6d137c},
  intrahash = {69645e07736cf5cb96efa1401a815cb0},
  journal = {Random Structures & Algorithms},
  pages = {161-179},
  title = {A critical point for random graphs with a given degree sequence},
  url = {/brokenurl#citeseer.ist.psu.edu/molloy95critical.html},
  volume = 6,
  year = 1995
}

@article{anderson1999ppl,
  author = {Anderson, C.J. and Wasserman, S. and Crouch, B.},
  interhash = {bc2bb58cfd833af662976fa8b73f4607},
  intrahash = {b2e086ec820f42183555e14de772f695},
  journal = {Social Networks},
  number = 1,
  pages = {37--66},
  publisher = {Elsevier},
  title = {{A p* primer: Logit models for social networks}},
  volume = 21,
  year = 1999
}

@article{snijders2002mcm,
  author = {Snijders, T.A.B.},
  interhash = {82953e285fb7bfcc79462f7c38bd8e54},
  intrahash = {b947cc90ef6f010d357c5048389032aa},
  journal = {Journal of Social Structure},
  number = 2,
  pages = {1--40},
  title = {{Markov chain Monte Carlo estimation of exponential random graph models}},
  volume = 3,
  year = 2002
}

@article{chebolu2008pagerank,
  author = {Chebolu, P. and Melsted, P.},
  booktitle = {Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms},
  interhash = {b186427a40b0af4a6414d82f0040613f},
  intrahash = {742b675a09d540687fc2c352a883d501},
  organization = {Society for Industrial and Applied Mathematics Philadelphia, PA, USA},
  pages = {1010--1018},
  title = {{PageRank and the random surfer model}},
  url = {http://scholar.google.de/scholar.bib?q=info:f7YaFVQIaeIJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=2},
  year = 2008
}

@article{loulwah2009topic,
  abstract = {Topic models, like Latent Dirichlet Allocation (LDA), have been recently used to automatically generate text corpora topics,
and to subdivide the corpus words among those topics. However, not all the estimated topics are of equal importance or correspondto genuine themes of the domain. Some of the topics can be a collection of irrelevant words, or represent insignificant themes.Current approaches to topic modeling perform manual examination to find meaningful topics. This paper presents the first automatedunsupervised analysis of LDA models to identify junk topics from legitimate ones, and to rank the topic significance. Basically,the distance between a topic distribution and three definitions of “junk distribution” is computed using a variety of measures,from which an expressive figure of the topic significance is implemented using 4-phase Weighted Combination approach. Ourexperiments on synthetic and benchmark datasets show the effectiveness of the proposed approach in ranking the topic significance.},
  author = {AlSumait, Loulwah and Barbará, Daniel and Gentle, James and Domeniconi, Carlotta},
  interhash = {273b61715108282ac89350ba18f99eb2},
  intrahash = {6310cb442c4e7852070e4f631fa2c1fa},
  journal = {Machine Learning and Knowledge Discovery in Databases},
  pages = {67--82},
  title = {Topic Significance Ranking of LDA Generative Models},
  url = {http://dx.doi.org/10.1007/978-3-642-04180-8_22},
  year = 2009
}

@book{jordan-learning-98,
  editor = {Jordan, M.},
  interhash = {dca14c475ead34e75711dfe8bb911d96},
  intrahash = {101d8938173add30b69dd1f4872e6eb7},
  publisher = {MIT Press},
  title = {Learning in Graphical Models},
  year = 1998
}