Community Detection References
Updated: February 2012
- Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, and
Eric P. Xing. 2008. Mixed Membership Stochastic Blockmodels.
J. Mach. Learn. Res. 9 (June 2008), 1981-2014.
Accessed March 29, 2012 at
http://jmlr.csail.mit.edu/papers/volume9/airoldi08a/airoldi08a.pdf.
- Backstrom,L., Huttenlocher, D., Kleinberg, J. and Lan, X. (2006). Group formation in large social networks:
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.
ACM. ISBN 1-59593-339-5. Accessed January 27, 2012 at DOI: 10.1145/1150402.1150412
- Ball, B., Karrer, B., and Newman, M.E.J. (2011). An efficient and
principled method for detecting communities in networks. Phys. Rev.
E 84, 036103. Accessed March 18, 2012 at
http://arxiv.org/pdf/1104.3590v1.pdf. 14 pages.
- 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
http://www.pnas.org/content/106/50/21068.full.pdf+html.
- Brand, M. (2006). Fast low-rank modifications of the thin singular
value decomposition. Linear Algebra and Its Applications.
415 (1), 20–30. Accessed March 27, 2012 from
http://www.stat.osu.edu/~dmsl/thinSVDtracking.pdf
- 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
March 18, 2012 at http://users.cis.fiu.edu/~lzhen001/activities/KDD_USB_key_2010/workshops/W01%20MLG2010/p62-gyenge.pdf
- K. Henderson and T. Eliassi-Rad. Applying latent Dirichlet
allocation to group discovery in large graphs. In Proceedings of the
2009 ACM Symposium on Applied Computing, pp. 1456–1461, Association of
Computing Machinery. New York (2009). Accessed March 18, 2012 at
http://eliassi.org/papers/henderson-sac09.pdf.
- Karrer, B. and Newman, M. E. J. (2011). Stochastic blockmodels and
community structure in networks. Phys. Rev. E 83, 016107. Accessed
March 22, 2012 at
http://arxiv.org/abs/1008.3926.
- B. Long, Z.M. Zhang, X. Wu, and P. S. Yu. Spectral clustering for multi-type relational data. In
ICML ’06: Proceedings of the 23rd international conference on Machine
learning, pages 585–592,
New York, NY, USA, 2006. ACM. ISBN 1-59593-383-2.
DOI: 10.1145/1143844.1143918
- 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
structure in networks. Nature Physics 8, 25-31. Accessed March 22, 2012 at
http://www-personal.umich.edu/~mejn/papers/npcommunities.pdf.
- 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
with Graphs, Association of Computing Machinery, New
York (2009). Accessed March 18, 2012 at
http://research.ics.tkk.fi/publications/sami/mlg09sibm.pdf.
- 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
http://www.robots.ox.ac.uk/~yannis/psorakis_roberts_sheldon_nips2010.pdf.
- L. Tang, X. Wang, and H. Liu. (2009). Uncovering groups via
heterogeneous interaction analysis. In ICDM
’09: Proceedings of IEEE International Conference on Data Mining, pages 503–512. Accessed March 22, 2012 at
http://www.public.asu.edu/~ltang9/papers/multi-dimensional_network.pdf.
- 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
for Large-Scale Social Networks. In Proceedings of the
IEEE International Conference on Intelligence and Security Informatics.
pp. 200--207 (2007).
Accessed March 18, 2012 at
http://clgiles.ist.psu.edu/papers/ISI2007-LDA-SNA.pdf.
Some Data Sources
Software