@inproceedings{hotho2006trend,
  abstract = {As the number of resources on the web exceeds by far the number ofdocuments one can track, it becomes increasingly difficult to remainup to date on ones own areas of interest. The problem becomes moresevere with the increasing fraction of multimedia data, from whichit is difficult to extract some conceptual description of theircontents.One way to overcome this problem are social bookmark tools, whichare rapidly emerging on the web. In such systems, users are settingup lightweight conceptual structures called folksonomies, andovercome thus the knowledge acquisition bottleneck. As more and morepeople participate in the effort, the use of a common vocabularybecomes more and more stable. We present an approach for discoveringtopic-specific trends within folksonomies. It is based on adifferential adaptation of the PageRank algorithm to the triadichypergraph structure of a folksonomy. The approach allows for anykind of data, as it does not rely on the internal structure of thedocuments. In particular, this allows to consider different datatypes in the same analysis step. We run experiments on a large-scalereal-world snapshot of a social bookmarking system.},
  address = {Heidelberg},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proc. First International Conference on Semantics And Digital Media Technology (SAMT) },
  date = {2006-12-13},
  editor = {Avrithis, Yannis S. and Kompatsiaris, Yiannis and Staab, Steffen and O'Connor, Noel E.},
  ee = {http://dx.doi.org/10.1007/11930334_5},
  interhash = {227be738c5cea57530d592463fd09abd},
  intrahash = {42cda5911e901eadd0ac6a106a6aa1dc},
  isbn = {3-540-49335-2},
  month = {December},
  pages = {56-70},
  publisher = {Springer},
  series = {LNCS},
  title = {Trend Detection in Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf},
  vgwort = {27},
  volume = 4306,
  year = 2006
}

@article{newman2004finding,
  abstract = {We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.},
  author = {Newman, M E and Girvan, M},
  interhash = {b9145040e35ccb4d2a0ce18105e64ff4},
  intrahash = {0c522f0a01f72638e70916f1144746e6},
  journal = {Phys Rev E Stat Nonlin Soft Matter Phys},
  month = Feb,
  number = 2,
  pages = {026113.1-15},
  pmid = {14995526},
  title = {Finding and evaluating community structure in networks},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/14995526},
  volume = 69,
  year = 2004
}