@article{yang2010detecting,
  abstract = {Although a large body of work is devoted to finding communities in static social networks, only a few studies examined the dynamics of communities in evolving social networks. In this paper, we propose a dynamic stochastic block model for finding communities and their evolution in a dynamic social network. The proposed model captures the evolution of communities by explicitly modeling the transition of community memberships for individual nodes in the network. Unlike many existing approaches for modeling social networks that estimate parameters by their most likely values (i.e., point estimation), in this study, we employ a Bayesian treatment for parameter estimation that computes the posterior distributions for all the unknown parameters. This Bayesian treatment allows us to capture the uncertainty in parameter values and therefore is more robust to data noise than point estimation. In addition, an efficient algorithm is developed for Bayesian inference to handle large sparse social networks. Extensive experimental studies based on both synthetic data and real-life data demonstrate that our model achieves higher accuracy and reveals more insights in the data than several state-of-the-art algorithms.},
  affiliation = {Department of Computer Science and Engineering Michigan State University, East Lansing, MI 48824, USA},
  author = {Yang, Tianbao and Chi, Yun and Zhu, Shenghuo and Gong, Yihong and Jin, Rong},
  doi = {10.1007/s10994-010-5214-7},
  interhash = {c3a7c9bcf8cb19da8fc70fae5a8898ec},
  intrahash = {f0a3306d1a907dd1aac1b4f26e6a4dd0},
  issn = {0885-6125},
  journal = {Machine Learning},
  keyword = {Computer Science},
  pages = {1-33},
  publisher = {Springer Netherlands},
  title = {Detecting communities and their evolutions in dynamic social networks—a Bayesian approach},
  url = {http://dx.doi.org/10.1007/s10994-010-5214-7},
  year = 2010
}

@misc{leskovec2010empirical,
  abstract = {  Detecting clusters or communities in large real-world graphs such as largesocial or information networks is a problem of considerable interest. Inpractice, one typically chooses an objective function that captures theintuition of a network cluster as set of nodes with better internalconnectivity than external connectivity, and then one applies approximationalgorithms or heuristics to extract sets of nodes that are related to theobjective function and that "look like" good communities for the application ofinterest. In this paper, we explore a range of network community detectionmethods in order to compare them and to understand their relative performanceand the systematic biases in the clusters they identify. We evaluate severalcommon objective functions that are used to formalize the notion of a networkcommunity, and we examine several different classes of approximation algorithmsthat aim to optimize such objective functions. In addition, rather than simplyfixing an objective and asking for an approximation to the best cluster of anysize, we consider a size-resolved version of the optimization problem.Considering community quality as a function of its size provides a much finerlens with which to examine community detection algorithms, since objectivefunctions and approximation algorithms often have non-obvious size-dependentbehavior.},
  author = {Leskovec, Jure and Lang, Kevin J. and Mahoney, Michael W.},
  interhash = {0e58de655596b2198f4a7001facd0c32},
  intrahash = {410a9cbea51ea5dd3c56aad26a0e11b2},
  note = {cite arxiv:1004.3539},
  title = {Empirical Comparison of Algorithms for Network Community Detection},
  url = {http://arxiv.org/abs/1004.3539},
  year = 2010
}

@inproceedings{cattuto2007emergent,
  address = {Dresden, Germany},
  author = {Cattuto, Ciro and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio},
  booktitle = {Proceedings of the European Confeence on Complex Systems},
  interhash = {9afde66e2d53e2f23bed303f7bda30af},
  intrahash = {3977cdaf1ce7a4c500ac5cfd5a91c9e5},
  month = {October},
  title = {Emergent Community Structure in Social Tagging Systems},
  year = 2007
}

@inproceedings{palla07-centrality,
  author = {Pollner, Peter and Palla, Gergely and Abel, Daniel and Vicsek, Andras and Farkas, Illes J. and Derenyi, Imre and Vicsek, Tamas},
  booktitle = {Proceedings of the European Conference of Complex Systems (ECCS'07)},
  interhash = {f72290a2eb48fee7ed33f7ea12a08acc},
  intrahash = {1963dda8661c18628be7c52abec93111},
  title = {Centrality properties of directed module members in social networks},
  year = 2007
}