Modelamiento de redes sociales múltiples

En este trabajo se propone un modelo estadístico para caracterizar simultáneamente dos o más redes sociales, donde interactúan el mismo conjunto de actores. Además de investigar las relaciones dentro de las redes, el modelo propuesto permite extrapolar la información entre redes, con el fin de obten...

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Autores:: Álvarez Monroy, Victor Nicolás

Tipo de recurso:: Masters Thesis

Fecha de publicación:: 2021

Institución:: Universidad Santo Tomás

Repositorio:: Universidad Santo Tomás

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Modelamiento de redes sociales múltiples
title	Modelamiento de redes sociales múltiples
spellingShingle	Modelamiento de redes sociales múltiples Markov Chain Monte Carlo Networks Bayesian statistics Latent space model Online social networks -- Users -- Statistics Latent variables Statistical models Redes sociales en línea Variables latentes Modelos estadísticos Cadena de Markov de Monte Carlo Estadística bayesiana Redes sociales Modelo de espacio latente
title_short	Modelamiento de redes sociales múltiples
title_full	Modelamiento de redes sociales múltiples
title_fullStr	Modelamiento de redes sociales múltiples
title_full_unstemmed	Modelamiento de redes sociales múltiples
title_sort	Modelamiento de redes sociales múltiples
dc.creator.fl_str_mv	Álvarez Monroy, Victor Nicolás
dc.contributor.advisor.spa.fl_str_mv	Sosa Martínez, Juan Camilo
dc.contributor.author.spa.fl_str_mv	Álvarez Monroy, Victor Nicolás
dc.contributor.orcid.spa.fl_str_mv	https://orcid.org/0000-0001-7432-4014
dc.contributor.cvlac.spa.fl_str_mv	https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000019698
dc.contributor.corporatename.none.fl_str_mv	Universidad Santo Tomás
dc.subject.keyword.spa.fl_str_mv	Markov Chain Monte Carlo Networks Bayesian statistics Latent space model Online social networks -- Users -- Statistics Latent variables Statistical models
topic	Markov Chain Monte Carlo Networks Bayesian statistics Latent space model Online social networks -- Users -- Statistics Latent variables Statistical models Redes sociales en línea Variables latentes Modelos estadísticos Cadena de Markov de Monte Carlo Estadística bayesiana Redes sociales Modelo de espacio latente
dc.subject.lemb.spa.fl_str_mv	Redes sociales en línea Variables latentes Modelos estadísticos
dc.subject.proposal.spa.fl_str_mv	Cadena de Markov de Monte Carlo Estadística bayesiana Redes sociales Modelo de espacio latente
description	En este trabajo se propone un modelo estadístico para caracterizar simultáneamente dos o más redes sociales, donde interactúan el mismo conjunto de actores. Además de investigar las relaciones dentro de las redes, el modelo propuesto permite extrapolar la información entre redes, con el fin de obtener mejores resultados en términos de bondad de ajuste y predicción. Esta propuesta se basa en una extensión jerárquica del modelo de espacio latente de distancias, que asume una posición social "global'' para cada actor, lo que permite estudiar de forma parsimoniosa los roles sociales desde varios puntos de vista. Las capacidades del modelo se ilustran utilizando varios conjuntos de datos reales, teniendo en cuenta diferentes tipos de relaciones.
publishDate	2021
dc.date.accessioned.spa.fl_str_mv	2021-02-03T16:36:26Z
dc.date.available.spa.fl_str_mv	2021-02-03T16:36:26Z
dc.date.issued.spa.fl_str_mv	2021-01-20
dc.type.local.spa.fl_str_mv	Tesis de maestría
dc.type.version.none.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.category.spa.fl_str_mv	Formación de Recurso Humano para la Ctel: Trabajo de grado de Maestría
dc.type.coar.none.fl_str_mv	http://purl.org/coar/resource_type/c_bdcc
dc.type.drive.none.fl_str_mv	info:eu-repo/semantics/masterThesis
format	http://purl.org/coar/resource_type/c_bdcc
status_str	acceptedVersion
dc.identifier.citation.spa.fl_str_mv	Alvarez Monroy, V.N. (2021). Modelamiento de redes sociales múltiples. [Tesis de maestría, Universidad Santo Tomás Colombia]. Repositorio Institucional
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11634/31872
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional Universidad Santo Tomás
dc.identifier.instname.spa.fl_str_mv	instname:Universidad Santo Tomás
dc.identifier.repourl.spa.fl_str_mv	repourl:https://repository.usta.edu.co
identifier_str_mv	Alvarez Monroy, V.N. (2021). Modelamiento de redes sociales múltiples. [Tesis de maestría, Universidad Santo Tomás Colombia]. Repositorio Institucional reponame:Repositorio Institucional Universidad Santo Tomás instname:Universidad Santo Tomás repourl:https://repository.usta.edu.co
url	http://hdl.handle.net/11634/31872
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association, 88(422), 669-679. Aldous, D. J. (1985). Exchangeability and related topics. In École d'Été de Probabilités de Saint-Flour XIII—1983 (pp. 1-198). Springer, Berlin, Heidelberg. Aliverti, E., & Russo, M. (2020). Stratified stochastic variational inference for high-dimensional network factor model. arXiv preprint arXiv:2006.14217. Banerjee, A., Chandrasekhar, A. G., Duflo, E., & Jackson, M. O. (2013). The diffusion of microfinance. Science, 341(6144). Borg, I., & Groenen, P. J. (2005). Modern multidimensional scaling: Theory and applications. Springer Science & Business Media. D'Angelo, S., Alfò, M., & Fop, M. (2020). Model-based Clustering for Multivariate Networks. arXiv preprint arXiv:2001.05260. D'Angelo, S., Alfò, M., & Murphy, T. B. (2018, May). Node-specific effects in latent space modelling of multidimensional networks. In 49th Scientific meeting of the Italian Statistical Society. Durante, D., & Dunson, D. B. (2014). Nonparametric Bayes dynamic modelling of relational data. Biometrika, 101(4), 883-898. Durante, D., & Dunson, D. B. (2018). Bayesian inference and testing of group differences in brain networks. Bayesian Analysis, 13(1), 29-58. D’Angelo, S., Murphy, T. B., & Alfò, M. (2019). Latent space modelling of multidimensional networks with application to the exchange of votes in eurovision song contest. Annals of Applied Statistics, 13(2), 900-930. Gamerman, D., & Lopes, H. F. (2006). Markov chain Monte Carlo: stochastic simulation for Bayesian inference. CRC Press. Gao, L. L., Witten, D., & Bien, J. (2019). Testing for Association in Multi-View Network Data. arXiv preprint arXiv:1909.11640. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. CRC press. Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and computing, 24(6), 997-1016. Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 7(4), 457-472. Gollini, I., & Murphy, T. B. (2016). Joint modeling of multiple network views. Journal of Computational and Graphical Statistics, 25(1), 246-265. Green, P. J., & Hastie, D. I. (2009). Reversible jump MCMC. Genetics, 155(3), 1391-1403. Gupta, S., Sharma, G., & Dukkipati, A. (2018). Evolving Latent Space Model for Dynamic Networks. arXiv preprint arXiv:1802.03725. Haario, H., Saksman, E., & Tamminen, J. (2001). An adaptive Metropolis algorithm. Bernoulli, 7(2), 223-242. Han, Q., Xu, K., & Airoldi, E. (2015, June). Consistent estimation of dynamic and multi-layer block models. In International Conference on Machine Learning (pp. 1511-1520). Handcock, M. S., Raftery, A. E., & Tantrum, J. M. (2007). Model‐based clustering for social networks. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(2), 301-354. Hoff, P. (2008). Modeling homophily and stochastic equivalence in symmetric relational data. In Advances in neural information processing systems (pp. 657-664). Hoff, P. D. (2005). Bilinear mixed-effects models for dyadic data. Journal of the american Statistical association, 100(469), 286-295. Hoff, P. D. (2009). A first course in Bayesian statistical methods (Vol. 580). New York: Springer. Hoff, P. D. (2015). Multilinear tensor regression for longitudinal relational data. The annals of applied statistics, 9(3), 1169. Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the american Statistical association, 97(460), 1090-1098. Hoover, D. N. (1982). Row-column exchangeability and a generalized model for probability. Exchangeability in probability and statistics (Rome, 1981), 281-291. Kim, B., Lee, K. H., Xue, L., & Niu, X. (2018). A review of dynamic network models with latent variables. Statistics surveys, 12, 105. Kolaczyk, E. D., & Csárdi, G. (2014). Statistical analysis of network data with R (Vol. 65). New York, NY: Springer. Krackhardt, D. (1987). Cognitive social structures. Social networks, 9(2), 109-134. Handcock, M. S., & Krivitsky, P. N. (2008). Fitting Latent Cluster Models for Networks with latentnet. Journal of Statistical Software, 24(05). Krivitsky, P. N., Handcock, M. S., Raftery, A. E., & Hoff, P. D. (2009). Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models. Social networks, 31(3), 204-213. Li, W. J., Yeung, D. Y., & Zhang, Z. (2011). Generalized latent factor models for social network analysis. In Proceedings of the 22nd international joint conference on artificial intelligence (ijcai), barcelona, spain (p. 1705). Linkletter, C. D. (2007). Spatial process models for social network analysis (Doctoral dissertation, Simon Fraser University). Ma, Z., & Ma, Z. (2017). Exploration of large networks with covariates via fast and universal latent space model fitting. arXiv preprint arXiv:1705.02372. Minhas, S., Hoff, P. D., & Ward, M. D. (2019). Inferential approaches for network analysis: AMEN for latent factor models. Political Analysis, 27(2), 208-222. Nowicki, K., & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American statistical association, 96(455), 1077-1087. Paez, M. S., Amini, A. A., & Lin, L. (2019). Hierarchical stochastic block model for community detection in multiplex networks. arXiv preprint arXiv:1904.05330. Paul, S., & Chen, Y. (2016). Consistent community detection in multi-relational data through restricted multi-layer stochastic blockmodel. Electronic Journal of Statistics, 10(2), 3807-3870. Paul, S., & Chen, Y. (2020). Spectral and matrix factorization methods for consistent community detection in multi-layer networks. The Annals of Statistics, 48(1), 230-250. Polson, N. G., Scott, J. G., & Windle, J. (2013). Bayesian inference for logistic models using Pólya–Gamma latent variables. Journal of the American statistical Association, 108(504), 1339-1349. Raftery, A. E., Niu, X., Hoff, P. D., & Yeung, K. Y. (2012). Fast inference for the latent space network model using a case-control approximate likelihood. Journal of Computational and Graphical Statistics, 21(4), 901-919. Reyes, P., & Rodriguez, A. (2016). Stochastic blockmodels for exchangeable collections of networks. arXiv preprint arXiv:1606.05277. Roethlisberger, F. J., & Dickson, W. J. (2003). Management and the Worker (Vol. 5). Psychology press. Salter-Townshend, M., & McCormick, T. H. (2017). Latent space models for multiview network data. The annals of applied statistics, 11(3), 1217. Schweinberger, M., & Snijders, T. A. (2003). Settings in social networks: A measurement model. Sociological Methodology, 33(1), 307-341. Sewell, D. K., & Chen, Y. (2015). Latent space models for dynamic networks. Journal of the American Statistical Association, 110(512), 1646-1657. Sewell, D. K., & Chen, Y. (2016). Latent space models for dynamic networks with weighted edges. Social Networks, 44, 105-116. Sewell, D. K., & Chen, Y. (2017). Latent space approaches to community detection in dynamic networks. Bayesian Analysis, 12(2), 351-377. Sewell, D. K. (2019). Latent space models for network perception data. Netw. Sci., 7(2), 160-179. Sosa, J. (2017). A Latent Space Approach for Cognitive Social Structures Modeling and Graphical Record Linkage (Doctoral dissertation, UC Santa Cruz). Spencer, N. A., Junker, B., & Sweet, T. M. (2020). Faster MCMC for Gaussian Latent Position Network Models. arXiv preprint arXiv:2006.07687. Swartz, T. B., Gill, P. S., & Muthukumarana, S. (2015). A Bayesian approach for the analysis of triadic data in cognitive social structures. Journal of the Royal Statistical Society: Series C: Applied Statistics, 593-610. Turnbull, K. (2020). Advancements in latent space network modelling (Doctoral dissertation, Lancaster University). Wang, L., Zhang, Z., & Dunson, D. (2019). Common and individual structure of brain networks. The Annals of Applied Statistics, 13(1), 85-112. Watanabe, S., & Opper, M. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of machine learning research, 11(12). Zhang, X. (2020). Statistical Analysis for Network Data using Matrix Variate Models and Latent Space Models (Doctoral dissertation).
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spelling	Sosa Martínez, Juan CamiloÁlvarez Monroy, Victor Nicoláshttps://orcid.org/0000-0001-7432-4014https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000019698Universidad Santo Tomás2021-02-03T16:36:26Z2021-02-03T16:36:26Z2021-01-20Alvarez Monroy, V.N. (2021). Modelamiento de redes sociales múltiples. [Tesis de maestría, Universidad Santo Tomás Colombia]. Repositorio Institucionalhttp://hdl.handle.net/11634/31872reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coEn este trabajo se propone un modelo estadístico para caracterizar simultáneamente dos o más redes sociales, donde interactúan el mismo conjunto de actores. Además de investigar las relaciones dentro de las redes, el modelo propuesto permite extrapolar la información entre redes, con el fin de obtener mejores resultados en términos de bondad de ajuste y predicción. Esta propuesta se basa en una extensión jerárquica del modelo de espacio latente de distancias, que asume una posición social "global'' para cada actor, lo que permite estudiar de forma parsimoniosa los roles sociales desde varios puntos de vista. Las capacidades del modelo se ilustran utilizando varios conjuntos de datos reales, teniendo en cuenta diferentes tipos de relaciones.In this work, we propose a statistical model to simultaneously characterize two or more social networks, where the same set of actors interact with each other. In addition to investigate ties within networks, the proposed model shares information among networks in order to obtain better results in terms of both goodness-of-fit and prediction. Our proposal is based on a hierarchical extension of the latent space distance model, by assuming a ``global'' social position for every actor, which allows us to study parsimoniously social roles from several perspectives. The capabilities of the model are illustrated using several real datasets, taking into account different types of relationships.Magister en Estadística Aplicadahttp://unidadinvestigacion.usta.edu.coMaestríaapplication/pdfspaUniversidad Santo TomásMaestría Estadística AplicadaFacultad de EstadísticaAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Modelamiento de redes sociales múltiplesMarkov Chain Monte CarloNetworksBayesian statisticsLatent space modelOnline social networks -- Users -- StatisticsLatent variablesStatistical modelsRedes sociales en líneaVariables latentesModelos estadísticosCadena de Markov de Monte CarloEstadística bayesianaRedes socialesModelo de espacio latenteTesis de maestríainfo:eu-repo/semantics/acceptedVersionFormación de Recurso Humano para la Ctel: Trabajo de grado de Maestríahttp://purl.org/coar/resource_type/c_bdccinfo:eu-repo/semantics/masterThesisCRAI-USTA BogotáAlbert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association, 88(422), 669-679.Aldous, D. J. (1985). Exchangeability and related topics. In École d'Été de Probabilités de Saint-Flour XIII—1983 (pp. 1-198). Springer, Berlin, Heidelberg.Aliverti, E., & Russo, M. (2020). Stratified stochastic variational inference for high-dimensional network factor model. arXiv preprint arXiv:2006.14217.Banerjee, A., Chandrasekhar, A. G., Duflo, E., & Jackson, M. O. (2013). The diffusion of microfinance. Science, 341(6144).Borg, I., & Groenen, P. J. (2005). Modern multidimensional scaling: Theory and applications. Springer Science & Business Media.D'Angelo, S., Alfò, M., & Fop, M. (2020). Model-based Clustering for Multivariate Networks. arXiv preprint arXiv:2001.05260.D'Angelo, S., Alfò, M., & Murphy, T. B. (2018, May). Node-specific effects in latent space modelling of multidimensional networks. In 49th Scientific meeting of the Italian Statistical Society.Durante, D., & Dunson, D. B. (2014). Nonparametric Bayes dynamic modelling of relational data. Biometrika, 101(4), 883-898.Durante, D., & Dunson, D. B. (2018). Bayesian inference and testing of group differences in brain networks. Bayesian Analysis, 13(1), 29-58.D’Angelo, S., Murphy, T. B., & Alfò, M. (2019). Latent space modelling of multidimensional networks with application to the exchange of votes in eurovision song contest. Annals of Applied Statistics, 13(2), 900-930.Gamerman, D., & Lopes, H. F. (2006). Markov chain Monte Carlo: stochastic simulation for Bayesian inference. CRC Press.Gao, L. L., Witten, D., & Bien, J. (2019). Testing for Association in Multi-View Network Data. arXiv preprint arXiv:1909.11640.Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. CRC press.Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and computing, 24(6), 997-1016.Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 7(4), 457-472.Gollini, I., & Murphy, T. B. (2016). Joint modeling of multiple network views. Journal of Computational and Graphical Statistics, 25(1), 246-265.Green, P. J., & Hastie, D. I. (2009). Reversible jump MCMC. Genetics, 155(3), 1391-1403.Gupta, S., Sharma, G., & Dukkipati, A. (2018). Evolving Latent Space Model for Dynamic Networks. arXiv preprint arXiv:1802.03725.Haario, H., Saksman, E., & Tamminen, J. (2001). An adaptive Metropolis algorithm. Bernoulli, 7(2), 223-242.Han, Q., Xu, K., & Airoldi, E. (2015, June). Consistent estimation of dynamic and multi-layer block models. In International Conference on Machine Learning (pp. 1511-1520).Handcock, M. S., Raftery, A. E., & Tantrum, J. M. (2007). Model‐based clustering for social networks. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(2), 301-354.Hoff, P. (2008). Modeling homophily and stochastic equivalence in symmetric relational data. In Advances in neural information processing systems (pp. 657-664).Hoff, P. D. (2005). Bilinear mixed-effects models for dyadic data. Journal of the american Statistical association, 100(469), 286-295.Hoff, P. D. (2009). A first course in Bayesian statistical methods (Vol. 580). New York: Springer.Hoff, P. D. (2015). Multilinear tensor regression for longitudinal relational data. The annals of applied statistics, 9(3), 1169.Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the american Statistical association, 97(460), 1090-1098.Hoover, D. N. (1982). Row-column exchangeability and a generalized model for probability. Exchangeability in probability and statistics (Rome, 1981), 281-291.Kim, B., Lee, K. H., Xue, L., & Niu, X. (2018). A review of dynamic network models with latent variables. Statistics surveys, 12, 105.Kolaczyk, E. D., & Csárdi, G. (2014). Statistical analysis of network data with R (Vol. 65). New York, NY: Springer.Krackhardt, D. (1987). Cognitive social structures. Social networks, 9(2), 109-134.Handcock, M. S., & Krivitsky, P. N. (2008). Fitting Latent Cluster Models for Networks with latentnet. Journal of Statistical Software, 24(05).Krivitsky, P. N., Handcock, M. S., Raftery, A. E., & Hoff, P. D. (2009). Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models. Social networks, 31(3), 204-213.Li, W. J., Yeung, D. Y., & Zhang, Z. (2011). Generalized latent factor models for social network analysis. In Proceedings of the 22nd international joint conference on artificial intelligence (ijcai), barcelona, spain (p. 1705).Linkletter, C. D. (2007). Spatial process models for social network analysis (Doctoral dissertation, Simon Fraser University).Ma, Z., & Ma, Z. (2017). Exploration of large networks with covariates via fast and universal latent space model fitting. arXiv preprint arXiv:1705.02372.Minhas, S., Hoff, P. D., & Ward, M. D. (2019). Inferential approaches for network analysis: AMEN for latent factor models. Political Analysis, 27(2), 208-222.Nowicki, K., & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American statistical association, 96(455), 1077-1087.Paez, M. S., Amini, A. A., & Lin, L. (2019). Hierarchical stochastic block model for community detection in multiplex networks. arXiv preprint arXiv:1904.05330.Paul, S., & Chen, Y. (2016). Consistent community detection in multi-relational data through restricted multi-layer stochastic blockmodel. Electronic Journal of Statistics, 10(2), 3807-3870.Paul, S., & Chen, Y. (2020). Spectral and matrix factorization methods for consistent community detection in multi-layer networks. The Annals of Statistics, 48(1), 230-250.Polson, N. G., Scott, J. G., & Windle, J. (2013). Bayesian inference for logistic models using Pólya–Gamma latent variables. Journal of the American statistical Association, 108(504), 1339-1349.Raftery, A. E., Niu, X., Hoff, P. D., & Yeung, K. Y. (2012). Fast inference for the latent space network model using a case-control approximate likelihood. Journal of Computational and Graphical Statistics, 21(4), 901-919.Reyes, P., & Rodriguez, A. (2016). Stochastic blockmodels for exchangeable collections of networks. arXiv preprint arXiv:1606.05277.Roethlisberger, F. J., & Dickson, W. J. (2003). Management and the Worker (Vol. 5). Psychology press.Salter-Townshend, M., & McCormick, T. H. (2017). Latent space models for multiview network data. The annals of applied statistics, 11(3), 1217.Schweinberger, M., & Snijders, T. A. (2003). Settings in social networks: A measurement model. Sociological Methodology, 33(1), 307-341.Sewell, D. K., & Chen, Y. (2015). Latent space models for dynamic networks. Journal of the American Statistical Association, 110(512), 1646-1657.Sewell, D. K., & Chen, Y. (2016). Latent space models for dynamic networks with weighted edges. Social Networks, 44, 105-116.Sewell, D. K., & Chen, Y. (2017). Latent space approaches to community detection in dynamic networks. Bayesian Analysis, 12(2), 351-377.Sewell, D. K. (2019). Latent space models for network perception data. Netw. Sci., 7(2), 160-179.Sosa, J. (2017). A Latent Space Approach for Cognitive Social Structures Modeling and Graphical Record Linkage (Doctoral dissertation, UC Santa Cruz).Spencer, N. A., Junker, B., & Sweet, T. M. (2020). Faster MCMC for Gaussian Latent Position Network Models. arXiv preprint arXiv:2006.07687.Swartz, T. B., Gill, P. S., & Muthukumarana, S. (2015). A Bayesian approach for the analysis of triadic data in cognitive social structures. Journal of the Royal Statistical Society: Series C: Applied Statistics, 593-610.Turnbull, K. (2020). Advancements in latent space network modelling (Doctoral dissertation, Lancaster University).Wang, L., Zhang, Z., & Dunson, D. (2019). Common and individual structure of brain networks. The Annals of Applied Statistics, 13(1), 85-112.Watanabe, S., & Opper, M. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of machine learning research, 11(12).Zhang, X. (2020). 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