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Statistical clustering of temporal networks through a dynamic stochastic block model

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  • Catherine Matias
  • Vincent Miele

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  • Catherine Matias & Vincent Miele, 2017. "Statistical clustering of temporal networks through a dynamic stochastic block model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1119-1141, September.
    • Handle: RePEc:bla:jorssb:v:79:y:2017:i:4:p:1119-1141
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    References listed on IDEAS

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    1. Leroux, Brian G., 1992. "Maximum-likelihood estimation for hidden Markov models," Stochastic Processes and their Applications, Elsevier, vol. 40(1), pages 127-143, February.
    2. Christophe Ambroise & Catherine Matias, 2012. "New consistent and asymptotically normal parameter estimates for random‐graph mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(1), pages 3-35, January.
    3. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    4. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    5. Petter Holme, 2015. "Modern temporal network theory: a colloquium," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(9), pages 1-30, September.
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    Cited by:

    1. Hledik, Juraj & Rastelli, Riccardo, 2020. "A dynamic network model to measure exposure diversification in the Austrian interbank market," ESRB Working Paper Series 109, European Systemic Risk Board.
    2. Riccardo Rastelli & Michael Fop, 2020. "A stochastic block model for interaction lengths," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 485-512, June.
    3. Wei Zhao & S.N. Lahiri, 2022. "Estimation of the Parameters in an Expanding Dynamic Network Model," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 261-282, June.
    4. Li Guo & Wolfgang Karl Hardle & Yubo Tao, 2018. "A Time-Varying Network for Cryptocurrencies," Papers 1802.03708, arXiv.org, revised Nov 2022.
    5. Saint‐Clair Chabert‐Liddell & Pierre Barbillon & Sophie Donnet, 2022. "Impact of the mesoscale structure of a bipartite ecological interaction network on its robustness through a probabilistic modeling," Environmetrics, John Wiley & Sons, Ltd., vol. 33(2), March.
    6. Daizaburo Shizuka & Allison E Johnson & Leigh Simmons, 2020. "How demographic processes shape animal social networks," Behavioral Ecology, International Society for Behavioral Ecology, vol. 31(1), pages 1-11.
    7. Ludkin, Matthew, 2020. "Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    8. Lee, Kevin H. & Xue, Lingzhou & Hunter, David R., 2020. "Model-based clustering of time-evolving networks through temporal exponential-family random graph models," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    9. Bartolucci, Francesco & Marino, Maria Francesca & Pandolfi, Silvia, 2018. "Dealing with reciprocity in dynamic stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 86-100.
    10. Ovielt Baltodano L'opez & Roberto Casarin, 2022. "A Dynamic Stochastic Block Model for Multi-Layer Networks," Papers 2209.09354, arXiv.org.
    11. Joshua Daniel Loyal & Yuguo Chen, 2020. "Statistical Network Analysis: A Review with Applications to the Coronavirus Disease 2019 Pandemic," International Statistical Review, International Statistical Institute, vol. 88(2), pages 419-440, August.
    12. Fabio Ashtar Telarico, 2023. "Are Sanctions for Losers? [Les sanctions sont-elles destinées aux perdants ?]," Post-Print hal-04238902, HAL.
    13. Jiang, Binyan & Li, Jialiang & Yao, Qiwei, 2023. "Autoregressive networks," LSE Research Online Documents on Economics 119983, London School of Economics and Political Science, LSE Library.
    14. Marino, Maria Francesca & Pandolfi, Silvia, 2022. "Hybrid maximum likelihood inference for stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    15. Zhiwei Yang & Weigang Wu & Yishun Chen & Xiaola Lin & Jiannong Cao, 2018. "(Q, S)-distance model and counting algorithms in dynamic distributed systems," International Journal of Distributed Sensor Networks, , vol. 14(1), pages 15501477187, January.
    16. Vincent Miele & Catherine Matias & Stéphane Robin & Stéphane Dray, 2019. "Nine quick tips for analyzing network data," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-10, December.
    17. Chabert-Liddell, Saint-Clair & Barbillon, Pierre & Donnet, Sophie & Lazega, Emmanuel, 2021. "A stochastic block model approach for the analysis of multilevel networks: An application to the sociology of organizations," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    18. C Matias & T Rebafka & F Villers, 2018. "A semiparametric extension of the stochastic block model for longitudinal networks," Biometrika, Biometrika Trust, vol. 105(3), pages 665-680.
    19. Paul Riverain & Simon Fossier & Mohamed Nadif, 2023. "Poisson degree corrected dynamic stochastic block model," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 135-162, March.
    20. Fabio Ashtar Telarico, 2023. "Are sanctions for losers? A network study of trade sanctions," Papers 2310.08193, arXiv.org.
    21. Nathan B. Wikle & Ephraim M. Hanks & David P. Hughes, 2019. "A Dynamic Individual-Based Model for High-Resolution Ant Interactions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 589-609, December.
    22. Juraj Hledik & Riccardo Rastelli, 2018. "A dynamic network model to measure exposure diversification in the Austrian interbank market," Papers 1804.01367, arXiv.org, revised Aug 2018.
    23. Thorben Funke & Till Becker, 2019. "Stochastic block models: A comparison of variants and inference methods," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-40, April.
    24. Dragana M. Pavlović & Bryan R.L. Guillaume & Soroosh Afyouni & Thomas E. Nichols, 2020. "Multi‐subject stochastic blockmodels with mixed effects for adaptive analysis of individual differences in human brain network cluster structure," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 363-396, August.
    25. Jun Liu & Jiangzhou Wang & Binghui Liu, 2020. "Community Detection of Multi-Layer Attributed Networks via Penalized Alternating Factorization," Mathematics, MDPI, vol. 8(2), pages 1-20, February.

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