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A partially separable model for dynamic valued networks

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  • Kei, Yik Lun
  • Chen, Yanzhen
  • Madrid Padilla, Oscar Hernan

Abstract

The Exponential-family Random Graph Model (ERGM) is a powerful model to fit networks with complex structures. However, for dynamic valued networks whose observations are matrices of counts that evolve over time, the development of the ERGM framework is still in its infancy. To facilitate the modeling of dyad value increment and decrement, a Partially Separable Temporal ERGM is proposed for dynamic valued networks. The parameter learning algorithms inherit state-of-the-art estimation techniques to approximate the maximum likelihood, by drawing Markov chain Monte Carlo (MCMC) samples conditioning on the valued network from the previous time step. The ability of the proposed model to interpret network dynamics and forecast temporal trends is demonstrated with real data.

Suggested Citation

  • Kei, Yik Lun & Chen, Yanzhen & Madrid Padilla, Oscar Hernan, 2023. "A partially separable model for dynamic valued networks," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:csdana:v:187:y:2023:i:c:s0167947323001226
    DOI: 10.1016/j.csda.2023.107811
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    References listed on IDEAS

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    1. Tom Broekel & Marcel Bednarz, 2018. "Disentangling link formation and dissolution in spatial networks: An Application of a Two-Mode STERGM to a Project-Based R&D Network in the German Biotechnology Industry," Networks and Spatial Economics, Springer, vol. 18(3), pages 677-704, September.
    2. Mingqi Han & Eric A. Bushong & Mayuko Segawa & Alexandre Tiard & Alex Wong & Morgan R. Brady & Milica Momcilovic & Dane M. Wolf & Ralph Zhang & Anton Petcherski & Matthew Madany & Shili Xu & Jason T. , 2023. "Spatial mapping of mitochondrial networks and bioenergetics in lung cancer," Nature, Nature, vol. 615(7953), pages 712-719, March.
    3. Hunter, David R. & Handcock, Mark S. & Butts, Carter T. & Goodreau, Steven M. & Morris, Martina, 2008. "ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i03).
    4. Pavel N. Krivitsky & Mark S. Handcock, 2014. "A separable model for dynamic networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 29-46, January.
    5. Goyal, Ravi & De Gruttola, Victor, 2020. "Dynamic network prediction," Network Science, Cambridge University Press, vol. 8(4), pages 574-595, December.
    6. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2008. "Goodness of Fit of Social Network Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 248-258, March.
    7. Fuli Feng & Xiangnan He & Xiang Wang & Cheng Luo & Yiqun Liu & Tat-Seng Chua, 2018. "Temporal Relational Ranking for Stock Prediction," Papers 1809.09441, arXiv.org, revised Jan 2019.
    8. Krivitsky, Pavel N., 2017. "Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 149-161.
    9. Chen Zhang & Xinghua Dang & Tao Peng & Chaokai Xue, 2019. "Dynamic Evolution of Venture Capital Network in Clean Energy Industries Based on STERGM," Sustainability, MDPI, vol. 11(22), pages 1-25, November.
    10. Bruce A Desmarais & Skyler J Cranmer, 2012. "Statistical Inference for Valued-Edge Networks: The Generalized Exponential Random Graph Model," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-12, January.
    11. Caimo, Alberto & Gollini, Isabella, 2020. "A multilayer exponential random graph modelling approach for weighted networks," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
    12. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    13. Steffen Lauritzen & Alessandro Rinaldo & Kayvan Sadeghi, 2018. "Random networks, graphical models and exchangeability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 481-508, June.
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