Las redes están en todas partes. Desde los sistemas artificiales hasta los naturales, las redes son un componente fundamental para su entendimiento, y hacer ciencia sin “ciencia de redes” hoy resulta en un ejercicio incompleto.
En este taller le daremos una mirada general a las herramientas y métodos que han sido desarrollados (tanto desde la estadística como la econometría) para el análsis de las redes–con foco en las redes sociales–y sus efectos sobre, por ejemplo, el comportamiento humano.
Si bien parte del taller contempla el uso de R, este concentrará una parte importante del tiempo en el desarrollo de conceptos de modelamiento estadístico fundamentales asociados a los modelos que revisaremos a lo largo del taller. Al finalizar el taller se espera que los asistentes hayan adquirido la teoría estadística/econométrica e intuición elemental de los modelos revisados, y sepan que paquetes de R y referencias bibliográficas deberán explorar para entender más sobre estos modelos.
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Parte 1: Intro (presentacion)
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Parte 2: Exponential Random Graph Models (presentacion-problemas)
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Parte 3: Modelos espaciales y SOAM (presentacion)
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Parte 4: Aplicaciones (presentacion)
- Taller en Instituto Milenio de Fundamento de los Datos (IMFD), Diciembre 2018
Artítulos y libros citados:
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Aral, Sinan, Lev Muchnik, and Arun Sundararajan. 2009. “Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks.” Proceedings of the National Academy of Sciences of the United States of America 106 (51): 21544–9. https://doi.org/10.1073/pnas.0908800106.
Bivand, Roger S, Edzer J Pebesma, Virgilio Gomez-Rubio, and Edzer Jan Pebesma. 2008. Applied Spatial Data Analysis with R. Vol. 747248717. Springer.
Butts, Carter T. 2008. “4. A Relational Event Framework for Social Action.” Sociological Methodology 38 (1): 155–200. https://doi.org/10.1111/j.1467-9531.2008.00203.x.
Chandrasekhar, A. G., and M. O. Jackson. 2012. “Tractable and Consistent Random Graph Models.” ArXiv E-Prints, October. http://arxiv.org/abs/1210.7375.
Daraganova, G., and G. Robins. 2013. “Autologistic Actor Attribute Models.” Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, 102–14.
Desmarais, Skyler J., Bruce A. AND Cranmer. 2012. “Statistical Inference for Valued-Edge Networks: The Generalized Exponential Random Graph Model.” PLOS ONE 7 (1): 1–12. https://doi.org/10.1371/journal.pone.0030136.
Elhorst, J.P. 2013. Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. SpringerBriefs in Regional Science. Springer Berlin Heidelberg. https://books.google.cl/books?id=mu25BAAAQBAJ.
Frank, Ove, and David Strauss. 1986. “Markov Graphs.” Journal of the American Statistical Association 81 (395): 832–42. https://doi.org/10.1080/01621459.1986.10478342.
Geyer, Charles J., and Elizabeth A. Thompson. 1992. “Constrained Monte Carlo Maximum Likelihood for Dependent Data.” Journal of the Royal Statistical Society. Series B (Methodological) 54 (3): 657–99. http://www.jstor.org/stable/2345852.
Handcock, Mark S. 2003. “Assessing Degeneracy in Statistical Models of Social Networks.”
Hoff, Peter D, Adrian E Raftery, and Mark S Handcock. 2002. “Latent Space Approaches to Social Network Analysis.” Journal of the American Statistical Association 97 (460): 1090–8. https://doi.org/10.1198/016214502388618906.
Hunter, David R, and Mark S Handcock. 2006. “Inference in Curved Exponential Family Models for Networks.” Journal of Computational and Graphical Statistics 15 (3): 565–83. https://doi.org/10.1198/106186006X133069.
Hunter, David R., Mark S. Handcock, Carter T. Butts, Steven M. Goodreau, and Martina Morris. 2008. “ergm : A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks.” Journal of Statistical Software 24 (3). https://doi.org/10.18637/jss.v024.i03.
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Kashima, Yoshihisa, Samuel Wilson, Dean Lusher, Leonie J. Pearson, and Craig Pearson. 2013. “The Acquisition of Perceived Descriptive Norms as Social Category Learning in Social Networks.” Social Networks 35 (4): 711–19. https://doi.org/https://doi.org/10.1016/j.socnet.2013.06.002.
Kelejian, Harry H., and Ingmar R. Prucha. 2010. “Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances.” Journal of Econometrics 157 (1): 53–67. https://doi.org/10.1016/j.jeconom.2009.10.025.
King, Gary, and Richard Nielsen. 2016. “Why Propensity Scores Should Not Be Used for Matching.”
Krivitsky, Pavel N., and Mark S. Handcock. 2014. “A separable model for dynamic networks.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76 (1): 29–46. https://doi.org/10.1111/rssb.12014.
Lazega, Emmanuel, and Tom AB Snijders. 2015. Multilevel Network Analysis for the Social Sciences: Theory, Methods and Applications. Vol. 12. Springer.
LeSage, James P. 2008. “An Introduction to Spatial Econometrics.” Revue d’économie Industrielle 123 (123): 19–44. https://doi.org/10.4000/rei.3887.
LeSage, James P., and R Kelley Pace. 2014. “The Biggest Myth in Spatial Econometrics.” Econometrics 2 (4): 217–49. https://doi.org/10.2139/ssrn.1725503.
Lusher, Dean, Johan Koskinen, and Garry Robins. 2012. Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press.
Milo, Ron, Shalev Itzkovitz, Nadav Kashtan, Reuven Levitt, Shai Shen-Orr, Inbal Ayzenshtat, Michal Sheffer, and Uri Alon. 2004. “Superfamilies of Evolved and Designed Networks.” Science 303 (5663): 1538–42. https://doi.org/10.1126/science.1089167.
Moran, P. A. P. 1950. “Notes on Continuous Stochastic Phenomena.” Biometrika 37 (1/2): 17. https://doi.org/10.2307/2332142.
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Ripley, Ruth M, Tom AB Snijders, Zsofia Boda, András Vörös, and Paulina Preciado. 2011. “Manual for Rsiena.” University of Oxford, Department of Statistics, Nuffield College 1.
Ripley, Ruth M., Tom AB Snijders, Paulina Preciado, and Others. 2011. “Manual for RSIENA.” University of Oxford: Department of Statistics, Nuffield College, no. 2007. https://www.uni-due.de/hummell/sna/R/RSiena{\_}Manual.pdf.
Robbins, Herbert, and Sutton Monro. 1951. “A Stochastic Approximation Method.” Ann. Math. Statist. 22 (3): 400–407. https://doi.org/10.1214/aoms/1177729586.
Robins, Garry, Pip Pattison, Yuval Kalish, and Dean Lusher. 2007. “An Introduction to Exponential Random Graph (P*) Models for Social Networks.” Social Networks 29 (2): 173–91. https://doi.org/https://doi.org/10.1016/j.socnet.2006.08.002.
Robins, Garry, Tom Snijders, Peng Wang, Mark Handcock, and Philippa Pattison. 2007. “Recent Developments in Exponential Random Graph (P*) Models for Social Networks.” Social Networks 29 (2): 192–215. https://doi.org/https://doi.org/10.1016/j.socnet.2006.08.003.
Schweinberger, Michael, and Mark S. Handcock. 2015. “Local Dependence in Random Graph Models: Characterization, Properties and Statistical Inference.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 77 (3): 647–76. https://doi.org/10.1111/rssb.12081.
Sekhon, Jasjeet S. 2008. “The Neyman-Rubin Model of Causal Inference and Estimation via Matching Methods.” The Oxford Handbook of Political Methodology 2.
Shalizi, Cosma Rohilla, and Andrew C Thomas. 2011. “Homophily and Contagion Are Generically Confounded in Observational Social Network Studies.” Sociological Methods & Research 40 (2): 211–39. https://doi.org/10.1177/0049124111404820.
Snijders, Tom AB. 2002. “Markov Chain Monte Carlo Estimation of Exponential Random Graph Models.” Journal of Social Structure 3.
Snijders, Tom A. B. 2011. “Statistical Models for Social Networks.” Annual Review of Sociology 37 (1): 131–53. https://doi.org/10.1146/annurev.soc.012809.102709.
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Stadtfeld, Christoph, James Hollway, and Per Block. 2017. “Dynamic Network Actor Models: Investigating Coordination Ties Through Time.” Sociological Methodology 47 (1): 1–40. https://doi.org/10.1177/0081175017709295.
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