Improved Bayesian inference for the stochastic block model with application to large networks

Computational Statistics and Data Analysis

Volumn 60, Issue 1, 2013, Pages 12-31

  McDaid, Aaron F ^a   Murphy, Thomas Brendan ^a   Friel, Nial ^a   Hurley, Neil J ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

Blockmodeling; Clustering; Computational statistics; MCMC; Social networks

Indexed keywords

BLOCKMODELING; CLUSTERING; COMPUTATIONAL STATISTICS; MCMC; SOCIAL NETWORKS;

BAYESIAN NETWORKS; INFERENCE ENGINES; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

CLUSTERING ALGORITHMS;

EID: 84871763700     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2012.10.021     Document Type: Article

References (29)

1. E.M. Airoldi, D.M. Blei, S.E. Fienberg, and E.P. Xing Mixed membership stochastic blockmodels Journal of Machine Learning Research 9 2008 1981 2014
2. P.J. Bickel, and A. Chen A nonparametric view of network models and Newman-Girvan and other modularities Proceedings of the National Academy of Sciences 106 2009 21068 21073
3. C. Biernacki, G. Celeux, and G. Govaert Assessing a mixture model for clustering with the integrated completed likelihood IEEE Transactions on Pattern Analysis and Machine Intelligence 22 2000 719 725
4. Chan, J.; Lam, S.; Hayes, C.; 2011. Increasing the scalability of the fitting of generalised block models for social networks. In: International Joint Conference on Artificial Intelligence 2011, IJCAI.
5. J. Daudin, F. Picard, and S. Robin A mixture model for random graphs Statistics and Computing 18 2008 173 183
6. M. Everett Exact colorations of graphs and digraphs Social Networks 18 1996 319 331
7. S. Fortunato Community detection in graphs Physics Reports 486 2010 75 174
8. S. Gazal, J.J. Daudin, and S. Robin Accuracy of variational estimates for random graph mixture models Journal of Statistical Computation and Simulation 82 2011 849 862
9. M. Girvan, and M. Newman Community structure in social and biological networks Proceedings of the National Academy of Sciences of the United States of America 99 2002 7821
10. P.J. Green Reversible jump Markov chain Monte Carlo computation and Bayesian model determination Biometrika 82 1995 711 732
11. M.S. Handcock, A.E. Raftery, and J.M. Tantrum Model-based clustering for social networks Journal of the Royal Statistical Society: Series A (Statistics in Society) 170 2007 301 354
12. W.K. Hastings Monte Carlo sampling methods using Markov chains and their applications Biometrika 57 1970 97 109
13. P.D. Hoff, A.E. Raftery, and M.S. Handcock Latent space approaches to social network analysis Journal of the American Statistical Association 97 2002 1090 1098
14. J.M. Hofman, and C.H. Wiggins Bayesian approach to network modularity Physical Review Letters 100 2008 258701+
15. B. Karrer, and M.E.J. Newman Stochastic blockmodels and community structure in networks Physical Review E 83 2011 016107+
16. Kemp, C.; Griffiths, T.; Tenenbaum, J.; 2004. Discovering latent classes in relational data. MIT Technical Report.
17. P. Latouche, E. Birmele, and C. Ambroise Variational Bayesian inference and complexity control for stochastic block models Statistical Modelling 12 2012 93 115
18. J.S. Liu The collapsed Gibbs sampler in Bayesian computations with applications to a gene regulation problem Journal of the American Statistical Association 89 1994 958 966
19. M. Mariadassou, S. Robin, and C. Vacher Uncovering latent structure in valued graphs: a variational approach The Annals of Applied Statistics 4 2010 715 742
20. McDaid, A.F.; Murphy, B.T.; Friel, N.; Hurley, N.J.; 2012. Model-based clustering in networks with stochastic community finding. In: Compstat 2012.
21. M.E.J. Newman Fast algorithm for detecting community structure in networks Physical Review E 69 2004 066133+
22. M.E.J. Newman, and M. Girvan Finding and evaluating community structure in networks Physical Review E 69 2004 026113+
23. M. Newman, and E. Leicht Mixture models and exploratory analysis in networks Proceedings of the National Academy of Sciences of the United States of America 104 2007 9564
24. A. Nobile, and A. Fearnside Bayesian finite mixtures with an unknown number of components: the allocation sampler Statistics and Computing 17 2007 147 162
25. K. Nowicki, and T.A.B. Snijders Estimation and prediction for stochastic blockstructures Journal of the American Statistical Association 96 2001 1077 1087
26. G. Schwarz Estimating the dimension of a model The Annals of Statistics 6 1978 461 464
27. T.A.B. Snijders, and K. Nowicki Estimation and prediction for stochastic blockmodels for graphs with latent block structure Journal of Classification 14 1997 75 100
28. J. Wyse, and N. Friel Block clustering with collapsed latent block models Statistics and Computing 22 2012 415 428
29. H. Zanghi, C. Ambroise, and V. Miele Fast online graph clustering via Erds-Rényi mixture Pattern Recognition 41 2008 3592 3599