@article{reichardt-2006-74,
  author = {Reichardt, Joerg and Bornholdt, Stefan},
  interhash = {968ba40de011d4560e3d1279bee169ac},
  intrahash = {cc921cbcbe8baa31d94015c8e6b792a3},
  journal = {Physical Review E},
  pages = 016110,
  title = {Statistical Mechanics of Community Detection},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0603718},
  volume = 74,
  year = 2006
}

@inproceedings{conf/cvpr/ShiM97,
  author = {Shi, Jianbo and Malik, Jitendra},
  booktitle = {CVPR},
  ee = {http://computer.org/proceedings/cvpr/7822/78220731abs.htm},
  interhash = {600345c3af56da066873a30c9971a615},
  intrahash = {bc4607ac2084911e4b1ba23b323f649a},
  pages = {731-737},
  title = {Normalized Cuts and Image Segmentation.},
  url = {http://dblp.uni-trier.de/db/conf/cvpr/cvpr1997.html#ShiM97},
  year = 1997
}

@inproceedings{conf/ht/GibsonKR98,
  author = {Gibson, David and Kleinberg, Jon M. and Raghavan, Prabhakar},
  booktitle = {Hypertext},
  cdrom = {HT1998/P225.pdf},
  ee = {db/conf/ht/GibsonKR98.html},
  interhash = {47c85d35ba3293b0de52af32e824164b},
  intrahash = {bdc4ed454bc2dd7194de0f5f0b451203},
  pages = {225-234},
  title = {Inferring Web Communities from Link Topology.},
  url = {http://dblp.uni-trier.de/db/conf/ht/ht98.html#GibsonKR98},
  year = 1998
}

@proceedings{citeulike:3128,
  abstract = {We define a community on the web as a set of sites that have more links (in either direction) to members of the community than to non-members. Members of such a community can be efficiently identified in a maximum flow / minimum cut framework, where the source is composed of known members, and the sink consists of well-known non-members. A focused crawler that crawls to a fixed depth can approximate community membership by augmenting the graph induced by the crawl with links to a virtual sink...},
  address = {Boston, MA},
  author = {Flake, Gary and Lawrence, Steve and Giles, Lee L.},
  booktitle = {Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  citeulike-article-id = {3128},
  interhash = {fe87690693bc84a663c00371684da561},
  intrahash = {37985f2c54c08d5db2a28a24735bd9aa},
  keywords = {clustering max_flow web communities web_graph},
  month = {August February0--FebruaryMarch},
  pages = {150--160},
  title = {Efficient Identification of Web Communities},
  url = {http://citeseer.ist.psu.edu/flake00efficient.html},
  year = 2000
}

@misc{citeulike:591709,
  abstract = {We express community detection as an inference problem of determining the
most likely arrangement of communities. We then apply belief propagation and
mean-field theory to this problem, and show that this leads to fast, accurate
algorithms for community detection.},
  author = {Hastings, M. B.},
  citeulike-article-id = {591709},
  eprint = {cond-mat/0604429},
  interhash = {fe59f9ef2701365c4a7d31aee35a9f6e},
  intrahash = {74f8f223ecf17eb48210c77364580ebf},
  month = Apr,
  priority = {2},
  title = {Community Detection as an Inference Problem},
  url = {http://arxiv.org/abs/cond-mat/0604429},
  year = 2006
}

@misc{citeulike:95936,
  abstract = {The discovery and analysis of community structure in networks is a topic of
considerable recent interest within the physics community, but most methods
proposed so far are unsuitable for very large networks because of their
computational cost. Here we present a hierarchical agglomeration algorithm for
detecting community structure which is faster than many competing algorithms:
its running time on a network with n vertices and m edges is O(m d log n) where
d is the depth of the dendrogram describing the community structure. Many
real-world networks are sparse and hierarchical, with m ~ n and d ~ log n, in
which case our algorithm runs in essentially linear time, O(n log^2 n). As an
example of the application of this algorithm we use it to analyze a network of
items for sale on the web-site of a large online retailer, items in the network
being linked if they are frequently purchased by the same buyer. The network
has more than 400,000 vertices and 2 million edges. We show that our algorithm
can extract meaningful communities from this network, revealing large-scale
patterns present in the purchasing habits of customers.},
  author = {Clauset, Aaron and Newman, M. E. J. and Moore, Cristopher},
  citeulike-article-id = {95936},
  eprint = {cond-mat/0408187},
  interhash = {2c68e3c981a00380692a3b0b661d7cfd},
  intrahash = {f9a12630a6d31d576ea5222219a4cf0b},
  month = {August},
  priority = {0},
  title = {Finding community structure in very large networks},
  url = {http://arxiv.org/abs/cond-mat/0408187},
  year = 2004
}

@inproceedings{Staab00AI,
  author = {Staab, Steffen and Angele, J{\"{u}}rgen and Decker, Stefan and Hotho, Andreas and Maedche, Alexander and Schnurr, Hans-Peter and Studer, Rudi and Sure, York},
  booktitle = {AAAI 2000/IAAI 2000 - Proceedings of the 17th National Conference on Artificial Intelligence and 12th Innovative Applications of Artificial Intelligence Conference, Austin/TX, USA, July 30-August 3, 2000},
  interhash = {fe131db0ba88d5bba1220ba3912199c9},
  intrahash = {b1679932032a25b771f4b36f7f7b26c0},
  publisher = {AAAI Press/MIT Press},
  title = {AI for the Web - Ontology-based Community Web Portals},
  url = {http://www.aifb.uni-karlsruhe.de/WBS/Publ/2000/iaai_sstetal_2000.pdf},
  year = 2000
}

@inproceedings{staab.www9,
  author = {Staab, S. and Angele, J. and Decker, S. and Erdmann, M. and Hotho, A. and Maedche, A. and Schnurr, H.-P. and Studer, R. and Sure, Y.},
  booktitle = {WWW9 --- Proceedings of the 9th International World Wide Web              Conference, Amsterdam, The Netherlands},
  interhash = {93e9f10176d9f06be1658ff793f7c2ea},
  intrahash = {1b5d0eeeebd6bee17a7fb07d89ce476d},
  pages = {473-491},
  publisher = {Elsevier},
  title = {Semantic Community Web Portals},
  year = 2000
}