A Multiscale Community Blockmodel for Network Exploration

REFERENCES

• Airoldi , E. , Blei , D. , Fienberg , S. and Xing , E. 2008 . “Mixed Membership Stochastic Blockmodels,” . The Journal of Machine Learning Research , 9 : 1981 – 2014 .
   PubMed Web of Science ®Google Scholar
• Aldous , D. 1985 . “Exchangeability and Related Topics,” . In École d’Été de Probabilités de Saint-Flour XIII—1983(Lecture Notes in Mathematics, Vol. 1117) , Edited by: Aldous , D. J. , Ibragimov , I. A. , Jacod , J. and Hennequin , P.-L. 1 – 198 . Berlin : Springer .
   Google Scholar
• Batagelj , V. , Ferligoj , A. and Doreian , P. 1992 . “Direct and Indirect Methods for Structural Equivalence,” . Social Networks , 14 ( 1–2 ) : 63 – 90 .
   Web of Science ®Google Scholar
• Batagelj , V. and Mrvar , A. 1998 . “Pajek—Program for Large Network Analysis,” . Connections , 21 ( 2 ) : 47 – 57 .
   Google Scholar
• Bernardo , J. , Smith , A. and Berliner , M. 2000 . Bayesian Theory , New York : Wiley .
   Google Scholar
• Blackwell , D. and MacQueen , J. 1973 . “Ferguson Distributions via Pólya urn Schemes,” . The Annals of Statistics , 1 ( 2 ) : 353 – 355 .
   Web of Science ®Google Scholar
• Blei , D. , Griffiths , T. and Jordan , M. 2010 . “The Nested Chinese Restaurant Process and Bayesian Nonparametric Inference of Topic Hierarchies,” . Journal of the ACM , 57 ( 2 ) : 1 – 30 .
   Web of Science ®Google Scholar
• Blei , D. and Jordan , M. 2004 . “Variational Methods for the Dirichlet Process,” . In Proceedings of the Twenty-First International Conference on Machine Learning 12 Association for Computing Machinery (ACM)
   Google Scholar
• Blei , D. , Ng , A. and Jordan , M. 2003 . “Latent Dirichlet Allocation,” . The Journal of Machine Learning Research , 3 : 993 – 1022 .
   Web of Science ®Google Scholar
• Chung , F. 1997 . “Spectral Graph Theory,” . In Regional Conference Series in Mathematics American Mathematical Society , 1 – 212 . Providence, RI : American Mathematical Society . (Vol. 92)
   Google Scholar
• Clauset , A. , Moore , C. and Newman , M. 2008 . “Hierarchical Structure and the Prediction of Missing Links in Networks,” . Nature , 453 ( 7191 ) : 98 – 101 .
   PubMed Web of Science ®Google Scholar
• Clauset , A. , Newman , M. and Moore , C. 2004 . “Finding Community Structure in Very Large Networks,” . Physical Review E , 70 ( 6 ) : 66111
   PubMed Web of Science ®Google Scholar
• Dawah , H. , Hawkins , B. and Claridge , M. 1995 . “Structure of the Parasitoid Communities of Grass-Feeding Chalcid Wasps,” . Journal of Animal Ecology , 64 ( 6 ) : 708 – 720 .
   Web of Science ®Google Scholar
• Escobar , M. and West , M. 1995 . “Bayesian Density Estimation and Inference Using Mixtures,” . Journal of the American Statistical Association , 90 ( 430 ) : 577 – 588 .
   Web of Science ®Google Scholar
• Ferguson , T. 1973 . “A Bayesian Analysis of Some Nonparametric Problems,” . The Annals of Statistics , 1 ( 2 ) : 209 – 230 .
   Web of Science ®Google Scholar
• Girvan , M. and Newman , M. 2002 . “Community Structure in Social and Biological Networks,” . Proceedings of the National Academy of Sciences , 99 ( 12 ) : 7821 – 7826 .
   PubMed Web of Science ®Google Scholar
• Guimera , R. and Amaral , L. 2005 . “Functional Cartography of Complex Metabolic Networks,” . Nature , 433 : 895 – 900 .
   PubMed Web of Science ®Google Scholar
• Handcock , M. , Raftery , A. and Tantrum , J. 2007 . “Model-Based Clustering for Social Networks,” . Journal of the Royal Statistical Society, Series A , 170 ( 2 ) : 301 – 354 .
   Web of Science ®Google Scholar
• Hoff , P. , Raftery , A. and Handcock , M. 2002 . “Latent Space Approaches to Social Network Analysis,” . Journal of the American Statistical Association , 97 : 1090 – 1098 .
   Web of Science ®Google Scholar
• Jain , S. and Neal , R. 2004 . “A Split–Merge Markov Chain Monte Carlo Procedure for the Dirichlet Process Mixture Model,” . Journal of Computational and Graphical Statistics , 13 ( 1 ) : 158 – 182 .
   Web of Science ®Google Scholar
• Jordan , M. , Ghahramani , Z. , Jaakkola , T. and Saul , L. 1999 . “An Introduction to Variational Methods for Graphical Models,” . Machine Learning , 37 ( 2 ) : 183 – 233 .
   Web of Science ®Google Scholar
• KDD . 2010 . KDD Cup 2003—Datasets[online] , Ithaca , NY : Cornell University . Available at http://www.cs.cornell.edu/projects/kddcup/datasets.html
   Google Scholar
• Kemp , C. and Tenenbaum , J. 2008 . “The Discovery of Structural Form,” . Proceedings of the National Academy of Sciences , 105 ( 31 ) : 10687 – 10692 .
   PubMed Web of Science ®Google Scholar
• Kemp , C. , Tenenbaum , J. , Griffiths , T. , Yamada , T. and Ueda , N. 2006 . “Learning Systems of Concepts With an Infinite Relational Model,” . In Proceedings of the 21st National Conference on Artificial Intelligence,(Vol. 1) 381 – 388 . AAAI Press
   Google Scholar
• Krause , A. , Frank , K. , Mason , D. , Ulanowicz , R. and Taylor , W. 2003 . “Compartments Revealed in Food-Web Structure,” . Nature , 426 ( 6964 ) : 282 – 285 .
   PubMed Web of Science ®Google Scholar
• Krebs , V. 2002 . “Mapping Networks of Terrorist Cells,” . Connections , 24 ( 3 ) : 43 – 52 .
   Google Scholar
• Liu , J. 1994 . “The Collapsed Gibbs Sampler in Bayesian Computations With Applications to a Gene Regulation Problem,” . Journal of the American Statistical Association , 89 ( 427 ) : 958 – 966 .
   Web of Science ®Google Scholar
• MacEachern , S. and Müller , P. 1998 . “Estimating Mixture of Dirichlet Process Models,” . Journal of Computational and Graphical Statistics , 7 ( 2 ) : 223 – 238 .
   Web of Science ®Google Scholar
• Miller , K. , Griffiths , T. and Jordan , M. 2009 . “Nonparametric Latent Feature Models for Link Prediction,” . Advances in Neural Information Processing Systems (NIPS) , 22 : 1276 – 1284 .
   Google Scholar
• Mimno , D. and McCallum , A. 2007 . “Organizing the OCA: Learning Faceted Subjects From a Library of Digital Books,” . In Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries 376 – 385 . Association for Computing Machinery (ACM)
   Google Scholar
• Nallapati , R. , Ahmed , A. , Xing , E. and Cohen , W. 2008 . “Joint Latent Topic Models for Text and Citations,” . In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 542 – 550 . Association for Computing Machinery (ACM)
   Google Scholar
• Newman , D. , Chemudugunta , C. and Smyth , P. 2006 . “Statistical Entity-Topic Models,” . In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 680 – 686 . Association for Computing Machinery (ACM)
   Google Scholar
• Radicchi , F. , Castellano , C. , Cecconi , F. , Loreto , V. and Parisi , D. 2004 . “Defining and Identifying Communities in Networks,” . Proceedings of the National Academy of Sciences , 101 ( 9 ) : 2658 – 2663 .
   PubMed Web of Science ®Google Scholar
• Robert , C. and Casella , G. 2004 . Monte Carlo Statistical Methods , New York : Springer-Verlag .
   Google Scholar
• Roy , D. , Kemp , C. , Mansinghka , V. and Tenenbaum , J. 2007 . “Learning Annotated Hierarchies From Relational Data,” . Advances in Neural Information Processing Systems , 19 : 1185
   Google Scholar
• Teh , Y. , Jordan , M. , Beal , M. and Blei , D. 2006 . “Hierarchical Dirichlet Processes,” . Journal of the American Statistical Association , 101 ( 476 ) : 1566 – 1581 .
   Web of Science ®Google Scholar
• Teh , Y. and Roy , D. 2009 . “The Mondrian Process,” . Advances in Neural Information Processing Systems , 21 : 1377 – 1384 .
   Google Scholar
• Wainwright , M. and Jordan , M. 2008 . “Graphical Models, Exponential Families, and Variational Inference,” . Foundations and Trends in Machine Learning , 1 ( 1–2 ) : 1 – 305 .
   Google Scholar
• Wang , C. and Blei , D. 2009 . “Variational Inference for the Nested Chinese Restaurant Process,” . Advances in Neural Information Processing Systems , 22 : 1990 – 1998 .
   Google Scholar
• Wang , Y. and Wong , G. 1987 . “Stochastic Blockmodels for Directed Graphs,” . Journal of the American Statistical Association , 82 ( 397 ) : 8 – 19 .
   Web of Science ®Google Scholar
• Ward , J. 1963 . “Hierarchical Grouping to Optimize an Objective Function,” . Journal of the American Statistical Association , 58 ( 301 ) : 236 – 244 .
   Web of Science ®Google Scholar
• Xing , E. P. , Fu , W. and Song , L. 2010 . “A State-Space Mixed Membership Blockmodel for Dynamic Network Tomography,” . The Annals of Applied Statistics , 4 ( 2 ) : 535 – 566 .
   Web of Science ®Google Scholar