@inproceedings{atzmueller2013towards,
  address = {New York, NY, USA},
  author = {Atzmueller, Martin and Hilgenberg, Katy},
  booktitle = {Proc. 4th International Workshop on Modeling Social Media (MSM 2013), Hypertext 2013},
  interhash = {b0d93d41ff9e84514d614cd2b3507a1d},
  intrahash = {4ebea4979524a9c1c0d41845e41e33a9},
  publisher = {ACM Press},
  title = {{Towards Capturing Social Interactions with SDCF: An Extensible Framework for Mobile Sensing and Ubiquitous Data Collection}},
  year = 2013
}

@inproceedings{atzmueller2013sensor,
  address = {Hamburg, Germany},
  author = {Atzmueller, Martin and Hilgenberg, Katy},
  booktitle = {Proc. Sunbelt XXXIII: Annual Meeting of the International Network for Social Network Analysis},
  interhash = {b71797fb6ff8776761d5227a61875470},
  intrahash = {5f8a4602c1087ea93f1f7440050d1982},
  publisher = {INSNA},
  title = {{SDCF - A Sensor Data Collection Framework for Social and Ubiquitous Environments: Challenges and First Experiences in Sensor-based Social Networks (Abstract)}},
  year = 2013
}

@misc{clauset2007powerlaw,
  abstract = {Power-law distributions occur in many situations of scientific interest and
have significant consequences for our understanding of natural and man-made
phenomena. Unfortunately, the detection and characterization of power laws is
complicated by the large fluctuations that occur in the tail of the
distribution -- the part of the distribution representing large but rare events
-- and by the difficulty of identifying the range over which power-law behavior
holds. Commonly used methods for analyzing power-law data, such as
least-squares fitting, can produce substantially inaccurate estimates of
parameters for power-law distributions, and even in cases where such methods
return accurate answers they are still unsatisfactory because they give no
indication of whether the data obey a power law at all. Here we present a
principled statistical framework for discerning and quantifying power-law
behavior in empirical data. Our approach combines maximum-likelihood fitting
methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic
and likelihood ratios. We evaluate the effectiveness of the approach with tests
on synthetic data and give critical comparisons to previous approaches. We also
apply the proposed methods to twenty-four real-world data sets from a range of
different disciplines, each of which has been conjectured to follow a power-law
distribution. In some cases we find these conjectures to be consistent with the
data while in others the power law is ruled out.},
  author = {Clauset, Aaron and Shalizi, Cosma Rohilla and Newman, M. E. J.},
  doi = {10.1137/070710111},
  interhash = {2e3bc5bbd7449589e8bfb580e8936d4b},
  intrahash = {7da1624e601898dd74df839ce2daeb24},
  note = {cite arxiv:0706.1062Comment: 43 pages, 11 figures, 7 tables, 4 appendices; code available at  http://www.santafe.edu/~aaronc/powerlaws/},
  title = {Power-law distributions in empirical data},
  url = {http://arxiv.org/abs/0706.1062},
  year = 2007
}

@inproceedings{mitzlaff2011semantics,
  address = {Bamberg, Germany},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Proc. LWA 2013 (KDML Special Track)},
  interhash = {73088600a500f7d06768615d6e1c2b3d},
  intrahash = {820ffb2166b330bf60bb30b16e426553},
  publisher = {University of Bamberg},
  title = {{On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)}},
  year = 2011
}

@inproceedings{jaschke2013attribute,
  abstract = {We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.},
  author = {Jäschke, Robert and Rudolph, Sebastian},
  booktitle = {Contributions to the 11th International Conference on Formal Concept Analysis},
  editor = {Cellier, Peggy and Distel, Felix and Ganter, Bernhard},
  interhash = {000ab7b0ae3ecd1d7d6ceb39de5c11d4},
  intrahash = {45e900e280661d775d8da949baee3747},
  month = may,
  organization = {Technische Universität Dresden},
  pages = {19--34},
  title = {Attribute Exploration on the Web},
  url = {http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-113133},
  urn = {urn:nbn:de:bsz:14-qucosa-113133},
  year = 2013
}