暂无分享,去创建一个
[1] Kathryn B. Laskey,et al. Stochastic blockmodels: First steps , 1983 .
[2] Roger Guimerà,et al. A Network Inference Method for Large-Scale Unsupervised Identification of Novel Drug-Drug Interactions , 2013, PLoS Comput. Biol..
[3] Tiago P Peixoto,et al. Parsimonious module inference in large networks. , 2012, Physical review letters.
[4] Hagai Attias,et al. A Variational Bayesian Framework for Graphical Models , 1999 .
[5] Aaron Clauset,et al. Scoring dynamics across professional team sports: tempo, balance and predictability , 2013, EPJ Data Science.
[6] Alain Celisse,et al. Consistency of maximum-likelihood and variational estimators in the Stochastic Block Model , 2011, 1105.3288.
[7] Tiago P. Peixoto. Hierarchical block structures and high-resolution model selection in large networks , 2013, ArXiv.
[8] J. Bader,et al. Dynamic Networks from Hierarchical Bayesian Graph Clustering , 2010, PloS one.
[9] Santo Fortunato,et al. Community detection in graphs , 2009, ArXiv.
[10] Aaron Clauset,et al. Adapting the Stochastic Block Model to Edge-Weighted Networks , 2013, ArXiv.
[11] P. Latouche,et al. Model selection and clustering in stochastic block models with the exact integrated complete data likelihood , 2013, 1303.2962.
[12] Edoardo M. Airoldi,et al. Mixed Membership Stochastic Blockmodels , 2007, NIPS.
[13] Mason A. Porter,et al. A network analysis of committees in the United States House of Representatives , 2005, ArXiv.
[14] Leto Peel,et al. Detecting Change Points in the Large-Scale Structure of Evolving Networks , 2014, AAAI.
[15] M. Newman,et al. Hierarchical structure and the prediction of missing links in networks , 2008, Nature.
[16] C. Matias,et al. Parameter identifiability in a class of random graph mixture models , 2010, 1006.0826.
[17] Cristopher Moore,et al. Model selection for degree-corrected block models , 2012, Journal of statistical mechanics.
[18] A. W. Kemp,et al. Kendall's Advanced Theory of Statistics. , 1994 .
[19] M. Newman,et al. Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.
[20] P. Latouche,et al. Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood , 2015 .
[21] Cristopher Moore,et al. Structural Inference of Hierarchies in Networks , 2006, SNA@ICML.
[22] Yuchung J. Wang,et al. Stochastic Blockmodels for Directed Graphs , 1987 .
[23] Florent Krzakala,et al. Comparative study for inference of hidden classes in stochastic block models , 2012, ArXiv.
[24] Tore Opsahl,et al. Clustering in weighted networks , 2009, Soc. Networks.
[25] Chris H Wiggins,et al. Bayesian approach to network modularity. , 2007, Physical review letters.
[26] Roger Guimerà,et al. Missing and spurious interactions and the reconstruction of complex networks , 2009, Proceedings of the National Academy of Sciences.
[27] Christian P. Robert,et al. The Bayesian choice : from decision-theoretic foundations to computational implementation , 2007 .
[28] Santo Fortunato,et al. World citation and collaboration networks: uncovering the role of geography in science , 2012, Scientific Reports.
[29] C. Matias,et al. New consistent and asymptotically normal parameter estimates for random‐graph mixture models , 2012 .
[30] Mark Newman,et al. Networks: An Introduction , 2010 .
[31] Andrew C. Thomas,et al. Valued Ties Tell Fewer Lies: Why Not To Dichotomize Network Edges With Thresholds , 2011, ArXiv.
[32] William T. Freeman,et al. Understanding belief propagation and its generalizations , 2003 .
[33] Daniel B. Larremore,et al. Efficiently inferring community structure in bipartite networks , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.
[34] Mark E. J. Newman,et al. Stochastic blockmodels and community structure in networks , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.
[35] Alessandro Vespignani,et al. Reaction–diffusion processes and metapopulation models in heterogeneous networks , 2007, cond-mat/0703129.