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Point process modelling for directed interaction networks

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  • Patrick O. Perry
  • Patrick J. Wolfe

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  • Patrick O. Perry & Patrick J. Wolfe, 2013. "Point process modelling for directed interaction networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(5), pages 821-849, November.
    • Handle: RePEc:bla:jorssb:v:75:y:2013:i:5:p:821-849
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    References listed on IDEAS

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    1. Fowler, James H., 2006. "Connecting the Congress: A Study of Cosponsorship Networks," Political Analysis, Cambridge University Press, vol. 14(4), pages 456-487, October.
    2. Mckenzie, David & Rapoport, Hillel, 2007. "Network effects and the dynamics of migration and inequality: Theory and evidence from Mexico," Journal of Development Economics, Elsevier, vol. 84(1), pages 1-24, September.
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    Cited by:

    1. S. Haleh S. Dizaji & Saeid Pashazadeh & Javad Musevi Niya, 2022. "A Comparative Study of Some Point Process Models for Dynamic Networks," Complexity, John Wiley & Sons, vol. 2022(1).
    2. Rumana Lakdawala & Joris Mulder & Roger Leenders, 2025. "Simulating relational event history data: why and how," Journal of Computational Social Science, Springer, vol. 8(4), pages 1-47, November.
    3. Verena Bauer & Dietmar Harhoff & Göran Kauermann, 2022. "A smooth dynamic network model for patent collaboration data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 97-116, March.
    4. Federica Bianchi & Francesco Bartolucci & Stefano Peluso & Antonietta Mira, 2020. "Longitudinal networks of dyadic relationships using latent trajectories: evidence from the European interbank market," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 711-739, August.
    5. S Chandna & S C Olhede & P J Wolfe, 2022. "Local linear graphon estimation using covariates [Representations for partially exchangeable arrays of random variables]," Biometrika, Biometrika Trust, vol. 109(3), pages 721-734.
    6. 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.
    7. Masoumeh Fallahi & Reza Pourtaheri & Farzad Eskandari, 2024. "The Multivariate Generalized Linear Hawkes Process in High Dimensions with Applications in Neuroscience," Methodology and Computing in Applied Probability, Springer, vol. 26(1), pages 1-25, March.
    8. Zappa, Paola & Vu, Duy Q., 2021. "Markets as networks evolving step by step: Relational Event Models for the interbank market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    9. Yijun Wang & Weiwei Wang, 2021. "Quantile estimation of semiparametric model with time-varying coefficients for panel count data," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-18, December.
    10. Yufeng Xia & Yangkuo Li & Xiaobing Zhao & Xuan Xu, 2025. "Investigating network structures in recurrent event data with discrete observation times," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 31(3), pages 543-573, July.
    11. Francesco Bartolucci & Antonietta Mira & Stefano Peluso, 2025. "Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks," 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. 19(4), pages 895-920, December.
    12. Fabio Vieira & Roger Leenders & Joris Mulder, 2024. "Fast meta-analytic approximations for relational event models: applications to data streams and multilevel data," Journal of Computational Social Science, Springer, vol. 7(2), pages 1823-1859, October.
    13. Long, Yuhang & Huang, Tao, 2022. "A note on a dynamic network model with homogeneous structure," Statistics & Probability Letters, Elsevier, vol. 184(C).
    14. Marco Tonellato & Stefano Tasselli & Guido Conaldi & Jürgen Lerner & Alessandro Lomi, 2024. "A Microstructural Approach to Self-Organizing: The Emergence of Attention Networks," Organization Science, INFORMS, vol. 35(2), pages 496-524, March.
    15. Rūta Juozaitienė & Ernst C. Wit, 2024. "Nodal Heterogeneity can Induce Ghost Triadic Effects in Relational Event Models," Psychometrika, Springer;The Psychometric Society, vol. 89(1), pages 151-171, March.
    16. Nynke M. D. Niezink & Paolo Campana, 2023. "When Things Turn Sour: A Network Event Study of Organized Crime Violence," Journal of Quantitative Criminology, Springer, vol. 39(3), pages 655-678, September.
    17. Vishesh Karwa & Pavel N. Krivitsky & Aleksandra B. Slavković, 2017. "Sharing social network data: differentially private estimation of exponential family random-graph models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 481-500, April.
    18. Eric W. Fox & Martin B. Short & Frederic P. Schoenberg & Kathryn D. Coronges & Andrea L. Bertozzi, 2016. "Modeling E-mail Networks and Inferring Leadership Using Self-Exciting Point Processes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 564-584, April.
    19. Wenqin Du & Bailey K. Fosdick & Wen Zhou, 2025. "Regression Modeling of the Count Relational Data with Exchangeable Dependencies," Papers 2502.11255, arXiv.org.
    20. Cornelius Fritz & Michael Lebacher & Göran Kauermann, 2020. "Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 275-299, August.
    21. Tomáš Diviák & Jürgen Lerner, 2025. "Understanding the Mechanisms that Drive Relational Events Dynamics and Structure in Corruption Networks," Journal of Quantitative Criminology, Springer, vol. 41(4), pages 523-547, December.

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