메뉴 건너뛰기




Volumn 2, Issue , 2015, Pages 1511-1520

Consistent estimation of dynamic and multi-layer block models

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; LEARNING SYSTEMS; MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATION; NETWORK LAYERS; SOCIAL NETWORKING (ONLINE); STOCHASTIC SYSTEMS;

EID: 84969824537     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (143)

References (49)
  • 1
    • 67849135609 scopus 로고    scopus 로고
    • Recovering time-varying networks of dependencies in social and biological studies
    • Ahmed, A. and Xing, E. P. (2009). Recovering time-varying networks of dependencies in social and biological studies. Proceedings of the National Academy of Sciences, 106(29): 11878-11883.
    • (2009) Proceedings of the National Academy of Sciences , vol.106 , Issue.29 , pp. 11878-11883
    • Ahmed, A.1    Xing, E.P.2
  • 3
    • 84899007980 scopus 로고    scopus 로고
    • Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
    • Airoldi, E. M., Costa, T. B., and Chan, S. H. (2013). Stochastic blockmodel approximation of a graphon: Theory and consistent estimation. In Advances in Neural Information Processing Systems 26, pages 692-700.
    • (2013) Advances in Neural Information Processing Systems , vol.26 , pp. 692-700
    • Airoldi, E.M.1    Costa, T.B.2    Chan, S.H.3
  • 4
    • 84907144758 scopus 로고    scopus 로고
    • Pseudo-likelihood methods for community detection in large sparse networks
    • Amini, A. A., Chen, A., Bickel, P. J., and Levina, E. (2013). Pseudo-likelihood methods for community detection in large sparse networks. The Annals of Statistics, 41(4):2097-2122.
    • (2013) The Annals of Statistics , vol.41 , Issue.4 , pp. 2097-2122
    • Amini, A.A.1    Chen, A.2    Bickel, P.J.3    Levina, E.4
  • 6
    • 84907151164 scopus 로고    scopus 로고
    • Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels
    • Bickel, P., Choi, D., Chang, X., and Zhang, H. (2013). Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels. The Annals of Statistics, 41(4):1922-1943.
    • (2013) The Annals of Statistics , vol.41 , Issue.4 , pp. 1922-1943
    • Bickel, P.1    Choi, D.2    Chang, X.3    Zhang, H.4
  • 7
    • 75849140057 scopus 로고    scopus 로고
    • A nonparametric view of network models and Newman-Girvan and other modularities
    • Bickel, P. J. and Chen, A. (2009). A nonparametric view of network models and Newman-Girvan and other modularities. Proceedings of the National Academy of Sciences, 106(50):21068-21073.
    • (2009) Proceedings of the National Academy of Sciences , vol.106 , Issue.50 , pp. 21068-21073
    • Bickel, P.J.1    Chen, A.2
  • 8
    • 84875385659 scopus 로고    scopus 로고
    • Consistency of maximum-likelihood and variational estimators in the stochastic block model
    • Celisse, A., Daudin, J.-J., and Pierre, L. (2012). Consistency of maximum-likelihood and variational estimators in the stochastic block model. Electronic Journal of Statistics, 6:1847-1899.
    • (2012) Electronic Journal of Statistics , vol.6 , pp. 1847-1899
    • Celisse, A.1    Daudin, J.-J.2    Pierre, L.3
  • 9
    • 84861614159 scopus 로고    scopus 로고
    • Stochastic blockmodels with a growing number of classes
    • Choi, D. S., Wolfe, P. J., and Airoldi, E. M. (2012). Stochastic blockmodels with a growing number of classes. Biometrika, 99:273-284.
    • (2012) Biometrika , vol.99 , pp. 273-284
    • Choi, D.S.1    Wolfe, P.J.2    Airoldi, E.M.3
  • 11
    • 84555195640 scopus 로고    scopus 로고
    • Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
    • Decelle, A., Krzakala, F., Moore, C., and Zdeborová, L. (2011). Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications. Physical Review E, 84(6):066106.
    • (2011) Physical Review E , vol.84 , Issue.6 , pp. 066106
    • Decelle, A.1    Krzakala, F.2    Moore, C.3    Zdeborová, L.4
  • 14
    • 33645311516 scopus 로고    scopus 로고
    • Reality mining: Sensing complex social systems
    • Eagle, N. and Pentland, A. (2006). Reality mining: sensing complex social systems. Personal and Ubiquitous Computing, 10(4):255-268.
    • (2006) Personal and Ubiquitous Computing , vol.10 , Issue.4 , pp. 255-268
    • Eagle, N.1    Pentland, A.2
  • 23
  • 26
    • 79951710564 scopus 로고    scopus 로고
    • Stochastic blockmodels and community structure in networks
    • Karrer, B. and Newman, M. (2011). Stochastic blockmodels and community structure in networks. Physical Review E, 83(1):016107.
    • (2011) Physical Review E , vol.83 , Issue.1 , pp. 016107
    • Karrer, B.1    Newman, M.2
  • 27
    • 84898968964 scopus 로고    scopus 로고
    • Nonparametric multi-group membership model for dynamic networks
    • Kim, M. and Leskovec, J. (2013). Nonparametric multi-group membership model for dynamic networks. In Advances in Neural Information Processing Systems 26, pages 1385-1393.
    • (2013) Advances in Neural Information Processing Systems , vol.26 , pp. 1385-1393
    • Kim, M.1    Leskovec, J.2
  • 28
    • 0002719797 scopus 로고
    • The hungarian method for the assignment problem
    • Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1-2):83-97.
    • (1955) Naval Research Logistics Quarterly , vol.2 , Issue.1-2 , pp. 83-97
    • Kuhn, H.W.1
  • 29
    • 84922512593 scopus 로고    scopus 로고
    • Consistency of spectral clustering in stochastic block models
    • Lei, J. and Rinaldo, A. (2014). Consistency of spectral clustering in stochastic block models. The Annals of Statistics, 43(1):215-237.
    • (2014) The Annals of Statistics , vol.43 , Issue.1 , pp. 215-237
    • Lei, J.1    Rinaldo, A.2
  • 31
    • 77952328459 scopus 로고    scopus 로고
    • Community structure in time-dependent, multiscale, and multiplex networks
    • Mucha, R J., Richardson, T., Macon, K., Porter, M. A., and Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980):876-878.
    • (2010) Science , vol.328 , Issue.5980 , pp. 876-878
    • Mucha, R.J.1    Richardson, T.2    Macon, K.3    Porter, M.A.4    Onnela, J.-P.5
  • 33
    • 37649028224 scopus 로고    scopus 로고
    • Finding and evaluating community structure in networks
    • Newman, M. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2):026113.
    • (2004) Physical Review E , vol.69 , Issue.2 , pp. 026113
    • Newman, M.1    Girvan, M.2
  • 36
    • 80052860256 scopus 로고    scopus 로고
    • Spectral clustering and the high-dimensional stochastic blockmodel
    • Rohe, K., Chatterjee, S., and Yu, B. (2011). Spectral clustering and the high-dimensional stochastic blockmodel. The Annals of Statistics, 39(4): 1878-1915.
    • (2011) The Annals of Statistics , vol.39 , Issue.4 , pp. 1878-1915
    • Rohe, K.1    Chatterjee, S.2    Yu, B.3
  • 38
    • 36849035459 scopus 로고    scopus 로고
    • Dynamic social network analysis using latent space models
    • Sarkar, P. and Moore, A. W. (2005). Dynamic social network analysis using latent space models. ACM SIGKDD Explorations Newsletter, 7(2):31-40.
    • (2005) ACM SIGKDD Explorations Newsletter , vol.7 , Issue.2 , pp. 31-40
    • Sarkar, P.1    Moore, A.W.2
  • 40
    • 77951207739 scopus 로고    scopus 로고
    • Modeling graphs using dot product representations
    • Scheinerman, E. R. and Tucker, K. (2010). Modeling graphs using dot product representations. Computational Statistics, 25(1): 1-16.
    • (2010) Computational Statistics , vol.25 , Issue.1 , pp. 1-16
    • Scheinerman, E.R.1    Tucker, K.2
  • 42
    • 77956372873 scopus 로고    scopus 로고
    • Multire-lational organization of large-scale social networks in an online world
    • Szell, M., Lambiotte, R., and Thurner, S. (2010). Multire-lational organization of large-scale social networks in an online world. Proceedings of the National Academy of Sciences, 107(31): 13636-13641.
    • (2010) Proceedings of the National Academy of Sciences , vol.107 , Issue.31 , pp. 13636-13641
    • Szell, M.1    Lambiotte, R.2    Thurner, S.3
  • 45
    • 84904649838 scopus 로고    scopus 로고
    • Dynamic stochastic blockmodels for time-evolving social networks
    • Xu, K. S. and Hero, A. O. (2014). Dynamic stochastic blockmodels for time-evolving social networks. IEEE Journal of Selected Topics in Signal Processing, 8(4):552-562.
    • (2014) IEEE Journal of Selected Topics in Signal Processing , vol.8 , Issue.4 , pp. 552-562
    • Xu, K.S.1    Hero, A.O.2
  • 48
    • 79851513504 scopus 로고    scopus 로고
    • Detecting communities and their evolutions in dynamic social networks-A Bayesian approach
    • Yang, T., Chi, Y., Zhu, S., Gong, Y., and Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks-a Bayesian approach. Machine Learning, 82(2): 157-189.
    • (2011) Machine Learning , vol.82 , Issue.2 , pp. 157-189
    • Yang, T.1    Chi, Y.2    Zhu, S.3    Gong, Y.4    Jin, R.5
  • 49
    • 84874798051 scopus 로고    scopus 로고
    • Consistency of community detection in networks under degree-corrected stochastic block models
    • Zhao, Y., Levina, E., and Zhu, J. (2012). Consistency of community detection in networks under degree-corrected stochastic block models. The Annals of Statistics, 40(4):2266-2292.
    • (2012) The Annals of Statistics , vol.40 , Issue.4 , pp. 2266-2292
    • Zhao, Y.1    Levina, E.2    Zhu, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.