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Volumn 38, Issue , 2015, Pages 1079-1087

Stochastic block transition models for dynamic networks

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; STOCHASTIC SYSTEMS;

EID: 84954327869     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (74)

References (21)
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    • Durante, D.1    Dunson, D.B.2
  • 8
    • 77958057679 scopus 로고    scopus 로고
    • Hierarchical multilinear models for multi-way data
    • P. D. Hoff. Hierarchical multilinear models for multi-way data. Computational Statistics and Data Analysis, 55(1):530-543, 2011.
    • (2011) Computational Statistics and Data Analysis , vol.55 , Issue.1 , pp. 530-543
    • Hoff, P.D.1
  • 12
    • 84898968964 scopus 로고    scopus 로고
    • Nonparametric multi-group membership model for dynamic networks
    • M. Kim and J. Leskovec. Nonparametric multi-group membership model for dynamic networks. In Advances in Neural Information Processing Systems 25, pages 1385-1393, 2013.
    • (2013) Advances in Neural Information Processing Systems , vol.25 , pp. 1385-1393
    • Kim, M.1    Leskovec, J.2
  • 13
    • 80052630407 scopus 로고    scopus 로고
    • A latent process model for time series of attributed random graphs
    • N. H. Lee and C. E. Priebe. A latent process model for time series of attributed random graphs. Statistical Inference for Stochastic Processes, 14(3):231-253, 2011.
    • (2011) Statistical Inference for Stochastic Processes , vol.14 , Issue.3 , pp. 231-253
    • Lee, N.H.1    Priebe, C.E.2
  • 15
    • 36849035459 scopus 로고    scopus 로고
    • Dynamic social network analysis using latent space models
    • P. Sarkar and A. W. Moore. Dynamic social network analysis using latent space models. ACM SIGKDD Explorations Newsletter, 7(2):31-40, 2005.
    • (2005) ACM SIGKDD Explorations Newsletter , vol.7 , Issue.2 , pp. 31-40
    • Sarkar, P.1    Moore, A.W.2
  • 19
    • 79960461257 scopus 로고    scopus 로고
    • A state-space mixed membership blockmodel for dynamic network tomography
    • E. P. Xing, W. Fu, and L. Song. A state-space mixed membership blockmodel for dynamic network tomography. The Annals of Applied Statistics, 4(2): 535-566, 2010.
    • (2010) The Annals of Applied Statistics , vol.4 , Issue.2 , pp. 535-566
    • Xing, E.P.1    Fu, W.2    Song, L.3
  • 20
    • 84904649838 scopus 로고    scopus 로고
    • Dynamic stochastic block-models for time-evolving social networks
    • K. S. Xu and A. O. Hero III. Dynamic stochastic block-models for time-evolving social networks. IEEE Journal of Selected Topics in Signal Processing, 8 (4):552-562, 2014.
    • (2014) IEEE Journal of Selected Topics in Signal Processing , vol.8 , Issue.4 , pp. 552-562
    • Xu, K.S.1    Hero, A.O.2
  • 21
    • 79851513504 scopus 로고    scopus 로고
    • Detecting communities and their evolutions in dynamic social networks-A Bayesian approach
    • T. Yang, Y. Chi, S. Zhu, Y. Gong, and R. Jin. Detecting communities and their evolutions in dynamic social networks-a Bayesian approach. Machine Learning, 82(2):157-189, 2011.
    • (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


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.