Community Detection References
Updated: February 2012
- Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, and
Eric P. Xing. 2008. Mixed Membership Stochastic Blockmodels.
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membership, growth, and evolution. In Proceedings of the 12th ACMSIGKDD international
conference on Knowledge discovery and data mining, pages 44-54,New York,NY, USA.
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principled method for detecting communities in networks. Phys. Rev.
E 84, 036103. Accessed March 18, 2012 at
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- P.J. Bickel and A. Chen. A nonparametric view of network models and
Newman–Girvan and other modularities. In Proceedings of the National Academy of Sciences USA 106, 21068–21073. (2009). Accessed March 18, 2012 at
- Brand, M. (2006). Fast low-rank modifications of the thin singular
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- Fortunato, S. (2010). Community Detection in Graphs. Physics Reports, 486(3-5), pp. 75-174. Accessed January 17, 2012 at http://arxiv.org/abs/0906.0612. 103 pages.
- A. Gyenge, J. Sinkkonen, and A. A. Benczur. An efficient
block model for clustering sparse graphs. In Proceedings
of the 8th International Workshop on Mining and Learning with Graphs,
pp. 62–69, Association of Computing Machinery, New York (2010). Accessed
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- K. Henderson and T. Eliassi-Rad. Applying latent Dirichlet
allocation to group discovery in large graphs. In Proceedings of the
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- B. Long, Z.M. Zhang, X. Wu, and P. S. Yu. Spectral clustering for multi-type relational data. In
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- Newman, M. E. J. and Girvan, M. (2004a). Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113. Accessed January 27, 2012 at http://arxiv.org/abs/cond-mat/0308217
- Newman, M. E. J. (2004b). Who is the best connected scientist? A study of scientific coauthorship networks. In Complex Networks, E. Ben-Naim, H. Frauenfelder, and Z. Toroczkai (eds.), pp. 337-370, Springer, Berlin. Accessed January 27, 2012 at http://www-personal.umich.edu/~mejn/papers/cnlspre.pdf. 32 pages.
- Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 036104. Accessed January 27, 2012 at http://arxiv.org/abs/physics/0605087
- Newman, M. E. J., Barabási, A.-L., and Watts, D. J. (2006b). The Structure and Dynamics of Networks. Princeton University Press.
- Newman, M. E. J. (2011). Communities, modules and large-scale
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- J. Parkinnen, A. Gyenge, J. Sinkkoken, and S. Kaski, A
block model suitable for sparse graphs. In Proceedings of
the 7th International Workshop on Mining and Learning
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- Porter, M.A., Onnela, J. & Mucha, P.J. (2009). Communities in Networks. Notices of the American Mathematical Society, 56(9), pp. 1082-1097, 1164-1166. Accessed January 17, 2012 at http://arxiv.org/abs/0902.3788. 27 pages.
- Psorakis, I., Roberts, S., and Sheldon, B. (2010).
Soft Partitioning in Networks via Bayesian Non-negative Matrix Factorization.
In Proceedings of the 24th Annual Conference on Neural Information Processing Systems (NIPS), Workshop on Networks Across Disciplines: Theory and Applications. Accessed March 22, 2012 at
- L. Tang, X. Wang, and H. Liu. (2009). Uncovering groups via
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- Tang, L. & Liu, H. (2010). Graph Mining Applications to Social Network Analysis. In C.C. Aggarwal and H. Wang (eds.) Managing and Mining Graph Data, chapter 16, pp. 487-513. Springer. Accessed January 17, 2012 at http://www.public.asu.edu/~ltang9/papers/graph_mining.pdf. 27 pages.
- Tang, L. & Liu, H. (2010). Community Detection and Mining in SocialMedia. Morgan & Claypool Publishers. Accessed January 17, 2012 at http://dmml.asu.edu/cdm/. 127 pages.
- H. Zhang, B. Qiu, C. L. Giles, H. C. Foley, and J. Yen. An
LDA-based Community Structure Discovery Approach
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Accessed March 18, 2012 at
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