@inproceedings{1281269,
  address = {New York, NY, USA},
  author = {Tantipathananandh, Chayant and Berger-Wolf, Tanya and Kempe, David},
  booktitle = {KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {http://doi.acm.org/10.1145/1281192.1281269},
  interhash = {9373b48866b4faa1941db0bee9265af0},
  intrahash = {27a4fb58300979d4dbe94e75422418bd},
  isbn = {978-1-59593-609-7},
  location = {San Jose, California, USA},
  pages = {717--726},
  publisher = {ACM},
  title = {A framework for community identification in dynamic social networks},
  url = {http://portal.acm.org/citation.cfm?doid=1281192.1281269},
  year = 2007
}

@inproceedings{Approximating2008Java,
  abstract = {In many social media applications, a small fraction of the members are highly linked while most are sparsely connected to the network. Such a skewed distribution is sometimes referred to as the"long tail". Popular applications like meme trackers and content aggregators mine for information from only the popular blogs located at the head of this curve. On the other hand, the long tail contains large volumes of interesting information and niches. The question we address in this work is how best to approximate the community membership of entities in the long tail using only a small percentage of the entire graph structure. Our technique utilizes basic linear algebra manipulations and spectral methods. It has the advantage of quickly and efficiently finding a reasonable approximation of the community structure of the overall network. Such a method has significant applications in blog analysis engines as well as social media monitoring tools in general. },
  author = {Java, Akshay and Joshi, Anupam and FininBook, Tim},
  booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)},
  date = {2008 Abstract:},
  interhash = {ede357e110fee8803dc181d262f30087},
  intrahash = {386f36679c111f30e37ced272d5b355c},
  publisher = {AAAI Press},
  title = {Approximating the Community Structure of the Long Tail},
  url = {http://ebiquity.umbc.edu/paper/html/id/381/Approximating-the-Community-Structure-of-the-Long-Tail},
  year = 2008
}

@inproceedings{conf/pkdd/CaiSHYH05,
  author = {Cai, Deng and Shao, Zheng and He, Xiaofei and Yan, Xifeng and Han, Jiawei},
  booktitle = {PKDD},
  crossref = {conf/pkdd/2005},
  date = {2005-11-14},
  editor = {Jorge, Alípio and Torgo, Luís and Brazdil, Pavel and Camacho, Rui and Gama, João},
  ee = {http://dx.doi.org/10.1007/11564126_44},
  interhash = {ffbc54eea27012bcd133f5b96fde010f},
  intrahash = {6045c9e8efdcbe1578f1f769c6b9274b},
  isbn = {3-540-29244-6},
  pages = {445-452},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Community Mining from Multi-relational Networks.},
  url = {http://dblp.uni-trier.de/db/conf/pkdd/pkdd2005.html#CaiSHYH05},
  volume = 3721,
  year = 2005
}