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All issues Volume 24 (2020) ESAIM: PS, 24 (2020) 718-738 References
Open Access
Issue
ESAIM: PS
Volume 24, 2020
Page(s) 718 - 738
DOI https://doi.org/10.1051/ps/2020025
Published online 16 November 2020
  1. E. Abbe, Community detection and stochastic block models. Found. Trends Commun. Inform. Theory 14 (2018) 1–162. [CrossRef] [Google Scholar]
  2. K.B. Athreya and P. Jagers (Eds.), Classical and Modern Branching Processes, Vol. 84. Springer Science & Business Media (2012). [Google Scholar]
  3. S. Athreya and A. Röllin, Respondent driven sampling and sparse graph convergence. Electron. Commun. Probab. 23 (2018) 3. [CrossRef] [Google Scholar]
  4. A. Bagheri and M. Saadati, Exploring the effectiveness of chain referral methods in sampling hidden populations. Indian J. Sci. Technol. 8 (2015) 1–8. [Google Scholar]
  5. A.D. Barbour, L. Holst and S. Janson, Poisson Approximation, Vol. 2 of Oxford Studies in Probability. The Clarendon Press, Oxford University Press, New York (1992). [Google Scholar]
  6. A. Barbour and G. Reiner, Approximating the epidemic curve. Electron. J. Probab. 18 (2013) 54. [Google Scholar]
  7. B. Bollobás and O. Riordan, Asymptotic normality of the size of the giant component via a random walk. J. Combinat. Theory Ser. B 102 (2012) 53–61. [CrossRef] [Google Scholar]
  8. P. Billingsley, Convergence of Probability Measures. John Wiley & Sons, New York (1968). [Google Scholar]
  9. T. Britton and E. Pardoux, Stochastic Epidemic Models with Inference. Springer (2019). [CrossRef] [Google Scholar]
  10. A. Cousien, J.S. Dhersin, V.C. Tran and T.P.T. Vo, Respondent driven sampling on sparse Erdös-rényi graphs. Inpreparation (2019). [Google Scholar]
  11. R. Durrett, Random Graph Dynamics, Vol. 200, No. 7. Cambridge University Press, Cambridge (2007). [Google Scholar]
  12. N. Enriquez, G. Faraud and L. Ménard, Limiting shape of the depth first search tree in an Erdös-Rényi graph. Random Struct. Algorith. 56 (2020) 501–516. [CrossRef] [Google Scholar]
  13. S.N. Ethier and T.G. Kurtz, Markov Processes Characterization and Convergence. John Wiley & Sons, New York (1986). [CrossRef] [Google Scholar]
  14. A. Gadde, E.E. Gad, S. Avestimehr and A. Ortega, Active learning for community detection in stochastic block models. In 2016 IEEE International Symposium on Information Theory (ISIT) (2016) 1889–1893. [CrossRef] [Google Scholar]
  15. M. Girvan and M.E.J. Newman, Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99 (2002) 7821–7826. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  16. L.A. Goodman, Snowball sampling. Ann. Math. Statist. (1961) 148–170. [CrossRef] [MathSciNet] [Google Scholar]
  17. D.D. Heckathorn, Respondent-driven sampling: a new approach to the study of hidden populations. Social Probl. 44 (1997) 74–99. [CrossRef] [Google Scholar]
  18. D.D. Heckathorn, Respondent-driven sampling II: deriving valid population estimates from chain-referral samples of hidden populations. Social Probl. 49 (2002) 11–34. [CrossRef] [Google Scholar]
  19. P.W. Holland, K.B. Laskey and S. Leinhardt, Stochastic blockmodels: first steps. Social Netw. 5 (1983) 109–137. [CrossRef] [Google Scholar]
  20. A. Jakubowski, On the Skorokhod topology. Ann. Inst. Henri Poincaré 22 (1986) 263–285. [Google Scholar]
  21. S. Janson, M. Luczak and P. Windridge, Law of large numbers for the SIR epidemic on a random graph with given degrees. Random Struct. Algorith. 45 (2014) 726–763. [CrossRef] [Google Scholar]
  22. A. Joffe and M. Métivier, Weak convergence of sequences of semimartingales with applications to multitype branching processes. Adv. Appl. Probab 18 (1986) 20–65. [Google Scholar]
  23. P. Barbillon, S. Donnet, E. Lazega and A. Bar-Hen, Stochastic Block Models for Multiplex networks: an application to networks of researchers. Preprint arXiv:1501.06444 (2015). [Google Scholar]
  24. M. Métivier, Semimartingales: A Course on Stochastic Processes, Vol. 2. Walter de Gruyter (2011). [Google Scholar]
  25. A. Shaghaghi, R.S. Bhopal and A. Sheikh, Approaches to recruiting ‘hard-to-reach’ populations into research: a review of the literature. Health Promotion Perspect. 1 (2011) 86. [Google Scholar]
  26. V.C. Tran, P. Moyal, L. Decreusefond and J.S. Dhersin, Limite en grand graphe d’un processus SIR décrivant la propagation d’une épidemie sur un réseau. Journées MAS et Journée en l’honneur de Jacques Neveu, August 2010, Talence, France (2010). [Google Scholar]
  27. R. Van Der Hofstad, Random Graphs and Complex Networks. Vol. 1. Cambridge University Press (2016). [Google Scholar]
  28. T.P.T. Vo, Exploration of Random Graphs by the Respondent Driven Sampling method. Ph.D. thesis, University Paris 13 (2020). [Google Scholar]

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