Implementación computacional de modelos de procesos espaciales para análisis de redes sociales
ilustraciones, graficas
- Autores:
-
Solano Velásquez, Jesús David
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/82234
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
TEORIA BAYESIANA DE DECISIONES ESTADISTICAS
Bayesian statistical decision theory
Cadenas de Markov
Monte Carlo
Bayesiana
Redes
Modelamiento estadístico
Markov Chains
Bayesian
Networks
Statistical modelling
- Rights
- openAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional
| id |
UNACIONAL2_069930ac0caea480d2e33a2478f37e9d |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/82234 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Implementación computacional de modelos de procesos espaciales para análisis de redes sociales |
| dc.title.translated.eng.fl_str_mv |
Computational implementation of spatial process models for social network analysis |
| title |
Implementación computacional de modelos de procesos espaciales para análisis de redes sociales |
| spellingShingle |
Implementación computacional de modelos de procesos espaciales para análisis de redes sociales 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores TEORIA BAYESIANA DE DECISIONES ESTADISTICAS Bayesian statistical decision theory Cadenas de Markov Monte Carlo Bayesiana Redes Modelamiento estadístico Markov Chains Bayesian Networks Statistical modelling |
| title_short |
Implementación computacional de modelos de procesos espaciales para análisis de redes sociales |
| title_full |
Implementación computacional de modelos de procesos espaciales para análisis de redes sociales |
| title_fullStr |
Implementación computacional de modelos de procesos espaciales para análisis de redes sociales |
| title_full_unstemmed |
Implementación computacional de modelos de procesos espaciales para análisis de redes sociales |
| title_sort |
Implementación computacional de modelos de procesos espaciales para análisis de redes sociales |
| dc.creator.fl_str_mv |
Solano Velásquez, Jesús David |
| dc.contributor.advisor.none.fl_str_mv |
Sosa Martínez, Juan Camilo |
| dc.contributor.author.none.fl_str_mv |
Solano Velásquez, Jesús David |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
| topic |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores TEORIA BAYESIANA DE DECISIONES ESTADISTICAS Bayesian statistical decision theory Cadenas de Markov Monte Carlo Bayesiana Redes Modelamiento estadístico Markov Chains Bayesian Networks Statistical modelling |
| dc.subject.lemb.spa.fl_str_mv |
TEORIA BAYESIANA DE DECISIONES ESTADISTICAS |
| dc.subject.lemb.eng.fl_str_mv |
Bayesian statistical decision theory |
| dc.subject.proposal.spa.fl_str_mv |
Cadenas de Markov Monte Carlo Bayesiana Redes Modelamiento estadístico |
| dc.subject.proposal.eng.fl_str_mv |
Markov Chains Bayesian Networks Statistical modelling |
| description |
ilustraciones, graficas |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-09-01T15:21:02Z |
| dc.date.available.none.fl_str_mv |
2022-09-01T15:21:02Z |
| dc.date.issued.none.fl_str_mv |
2022-09-01 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Model Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/82234 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/82234 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.indexed.spa.fl_str_mv |
RedCol LaReferencia |
| dc.relation.references.spa.fl_str_mv |
Barabási, A.-L. and Albert, R. (1999). Emergence of scaling in random networks. science, 286(5439):509–512. Bender, E. A. and Canfield, E. R. (1978). The asymptotic number of labeled graphs with given degree sequences. Journal of Combinatorial Theory, Series A, 24(3):296–307. Chib, S. and Greenberg, E. (1995). Understanding the metropolis-hastings algorithm. The american statistician, 49(4):327–335. Ciminelli, J. T., Love, T., and Wu, T. T. (2019). Social network spatial model. Spatial statistics, 29:129–144. Durante, D., Dunson, D. B., et al. (2018). Bayesian inference and testing of group differences in brain networks. Bayesian Analysis, 13(1):29–58. Frank, O. and Strauss, D. (1986). Markov graphs. Journal of the american Statistical association, 81(395):832–842. Gilbert, E. N. (1959). Random graphs. The Annals of Mathematical Statistics, 30(4):1141– 1144. Gonçalves, L., Subtil, A., Oliveira, M. R., and de Zea Bermudez, P. (2014). Roc curve estimation: An overview. REVSTAT-Statistical journal, 12(1):1–20. Handcock, M. S. and Krivitsky, P. N. (2008). Fitting latent cluster models for networks with latentnet. Journal of Statistical Software, 24(05). 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). Multiplicative latent factor models for description and prediction of social networks. Computational and mathematical organization theory, 15(4):261. Hoff, P. D., Raftery, A. E., and Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the american Statistical association, 97(460):1090–1098. Kim, B. (2018). Latent Modeling of Dynamic Social Networks. The Pennsylvania State University. Kolaczyk, E. D. and Csárdi, G. (2020). Statistical analysis of network data with R,2nd edn,. Springer. Linkletter, C. D. (2007). Spatial process models for social network analysis. PhD thesis, Citeseer. Padgett, J. F. and Ansell, C. K. (1993). Robust action and the rise of the medici, 1400-1434. American journal of sociology, 98(6):1259–1319. Raftery, A. E., Niu, X., Hoff, P. D., and 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. Salter-Townshend, M. and McCormick, T. H. (2017). Latent space models for multiview network data. The annals of applied statistics, 11(3):1217. Schweinberger, M. and Snijders, T. A. (2003). Settings in social networks: A measurement model. Sociological Methodology, 33(1):307–341. Sewell, D. K. (2019). Latent space models for network perception data. Network Science, 7(2):160–179. Sewell, D. K. and Chen, Y. (2015). Latent space models for dynamic networks. Journal of the American Statistical Association, 110(512):1646–1657. Sosa, J. and Betancourt, B. (2022). A latent space model for multilayer network data. Computational Statistics & Data Analysis, page 107432. Sosa, J. and Buitrago, L. (2021). A review of latent space models for social networks. Revista Colombiana de Estadística, 44(1):171–200. Sosa, J. and Rodríguez, A. (2021). A latent space model for cognitive social structures data. Social Networks, 65:85–97. Wang, L., Zhang, Z., Dunson, D., et al. (2019). Common and individual structure of brain networks. The Annals of Applied Statistics, 13(1):85–112. Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. nature, 393(6684):440–442. Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of anthropological research, 33(4):452–473. |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial-CompartirIgual 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Atribución-NoComercial-CompartirIgual 4.0 Internacional http://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
xv, 74 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ciencias - Maestría en Ciencias - Estadística |
| dc.publisher.department.spa.fl_str_mv |
Departamento de Estadística |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ciencias |
| dc.publisher.place.spa.fl_str_mv |
Bogotá, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Bogotá |
| institution |
Universidad Nacional de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/82234/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/82234/3/1015461454.2022.pdf.pdf https://repositorio.unal.edu.co/bitstream/unal/82234/4/1015461454.2022.pdf.pdf.jpg |
| bitstream.checksum.fl_str_mv |
b577153cc0e11f0aeb5fc5005dc82d8a aed1ad9119416af6cbcfa797cdb20e63 4e23712911a5dbe4194ebfbaaac00632 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
| _version_ |
1814089426715803648 |
| spelling |
Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sosa Martínez, Juan Camilo363bd2a69f8dead964b93ad3f568e0d6Solano Velásquez, Jesús David558456be5317ddfa9ebe1ccd801a3d752022-09-01T15:21:02Z2022-09-01T15:21:02Z2022-09-01https://repositorio.unal.edu.co/handle/unal/82234Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficasEl modelamiento estadístico de las redes permite identificar su distribución de probabilidad, imputar datos faltantes y realizar predicciones sobre la formación de enlaces. Los modelos latentes abordan el modelamiento desde una perspectiva marginal, incorporan dependencias no condicionales por medio de efectos aleatorios. Un caso particular de los modelos latentes es el modelo basado en procesos espaciales completamente Bayesiano que soluciona los problemas de sobreajuste del modelo de espacio latente de distancia. En este documento se realiza la implementación computacional del modelo y se realiza un estudio de sus bondades de ajuste y bondades de predicción a través de redes sintéticas y reales. El modelo tiene buenas cualidades para la replicación de las estadísticas observadas en la red y la estimación de la superficie latente. Sin embargo, el poder predictivo, medido a través del área bajo la curva (AUC por sus siglas en inglés) no supera el valor de 0.7. También se presenta una forma alternativa de ajustar el modelo usando el algoritmo de caso-control. El modelo basado en la log-verosimilitud estimada tiene una buena calidad de bondad de ajuste. (Texto tomado de la fuente)Statistical modeling of networks makes it possible to identify their probability distribution, impute missing data and make predictions about link formation. Latent models approach modeling from a marginal perspective, incorporating non-conditional dependencies through random effects. A particular case of latent models is the fully Bayesian spatial process-based model that solves the overfitting problems of the latent distance space model. In this paper the computational implementation of the model is performed and a study of its goodness of fit and goodness of prediction through synthetic and real networks is carried out. The model has good qualities for the replication of the statistics observed in the network and the estimation of the latent surface. However, the predictive power, as measured by the area under the curve (AUC), does not exceed 0.7. An alternative way of fitting the model using the case-control algorithm is also presented. The model based on the estimated log-likelihood has a good good goodness-of-fit quality.MaestríaMagíster en Ciencias - EstadísticaAnálisis de Redes Socialesxv, 74 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaDepartamento de EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresTEORIA BAYESIANA DE DECISIONES ESTADISTICASBayesian statistical decision theoryCadenas de MarkovMonte CarloBayesianaRedesModelamiento estadísticoMarkov ChainsBayesianNetworksStatistical modellingImplementación computacional de modelos de procesos espaciales para análisis de redes socialesComputational implementation of spatial process models for social network analysisTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionModelTexthttp://purl.org/redcol/resource_type/TMRedColLaReferenciaBarabási, A.-L. and Albert, R. (1999). Emergence of scaling in random networks. science, 286(5439):509–512.Bender, E. A. and Canfield, E. R. (1978). The asymptotic number of labeled graphs with given degree sequences. Journal of Combinatorial Theory, Series A, 24(3):296–307.Chib, S. and Greenberg, E. (1995). Understanding the metropolis-hastings algorithm. The american statistician, 49(4):327–335.Ciminelli, J. T., Love, T., and Wu, T. T. (2019). Social network spatial model. Spatial statistics, 29:129–144.Durante, D., Dunson, D. B., et al. (2018). Bayesian inference and testing of group differences in brain networks. Bayesian Analysis, 13(1):29–58.Frank, O. and Strauss, D. (1986). Markov graphs. Journal of the american Statistical association, 81(395):832–842.Gilbert, E. N. (1959). Random graphs. The Annals of Mathematical Statistics, 30(4):1141– 1144.Gonçalves, L., Subtil, A., Oliveira, M. R., and de Zea Bermudez, P. (2014). Roc curve estimation: An overview. REVSTAT-Statistical journal, 12(1):1–20.Handcock, M. S. and Krivitsky, P. N. (2008). Fitting latent cluster models for networks with latentnet. Journal of Statistical Software, 24(05).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). Multiplicative latent factor models for description and prediction of social networks. Computational and mathematical organization theory, 15(4):261.Hoff, P. D., Raftery, A. E., and Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the american Statistical association, 97(460):1090–1098.Kim, B. (2018). Latent Modeling of Dynamic Social Networks. The Pennsylvania State University.Kolaczyk, E. D. and Csárdi, G. (2020). Statistical analysis of network data with R,2nd edn,. Springer.Linkletter, C. D. (2007). Spatial process models for social network analysis. PhD thesis, Citeseer.Padgett, J. F. and Ansell, C. K. (1993). Robust action and the rise of the medici, 1400-1434. American journal of sociology, 98(6):1259–1319.Raftery, A. E., Niu, X., Hoff, P. D., and 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.Salter-Townshend, M. and McCormick, T. H. (2017). Latent space models for multiview network data. The annals of applied statistics, 11(3):1217.Schweinberger, M. and Snijders, T. A. (2003). Settings in social networks: A measurement model. Sociological Methodology, 33(1):307–341.Sewell, D. K. (2019). Latent space models for network perception data. Network Science, 7(2):160–179.Sewell, D. K. and Chen, Y. (2015). Latent space models for dynamic networks. Journal of the American Statistical Association, 110(512):1646–1657.Sosa, J. and Betancourt, B. (2022). A latent space model for multilayer network data. Computational Statistics & Data Analysis, page 107432.Sosa, J. and Buitrago, L. (2021). A review of latent space models for social networks. Revista Colombiana de Estadística, 44(1):171–200.Sosa, J. and Rodríguez, A. (2021). A latent space model for cognitive social structures data. Social Networks, 65:85–97.Wang, L., Zhang, Z., Dunson, D., et al. (2019). Common and individual structure of brain networks. The Annals of Applied Statistics, 13(1):85–112.Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. nature, 393(6684):440–442.Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of anthropological research, 33(4):452–473.EstudiantesInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-84675https://repositorio.unal.edu.co/bitstream/unal/82234/1/license.txtb577153cc0e11f0aeb5fc5005dc82d8aMD51ORIGINAL1015461454.2022.pdf.pdf1015461454.2022.pdf.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf3539199https://repositorio.unal.edu.co/bitstream/unal/82234/3/1015461454.2022.pdf.pdfaed1ad9119416af6cbcfa797cdb20e63MD53THUMBNAIL1015461454.2022.pdf.pdf.jpg1015461454.2022.pdf.pdf.jpgGenerated Thumbnailimage/jpeg4833https://repositorio.unal.edu.co/bitstream/unal/82234/4/1015461454.2022.pdf.pdf.jpg4e23712911a5dbe4194ebfbaaac00632MD54unal/82234oai:repositorio.unal.edu.co:unal/822342023-08-08 23:04:13.98Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |