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Spatial Process Models for Social Network Analysis

  • Juan Sosa [1] ; Jesús David Solano Velásquez [1]
    1. [1] Universidad Nacional de Colombia

      Universidad Nacional de Colombia

      Colombia

  • Localización: Comunicaciones en Estadística, ISSN 2027-3355, ISSN-e 2339-3076, Vol. 18, Nº. 1, 2025, págs. 87-101
  • Idioma: inglés
  • Títulos paralelos:
    • Modelos de Procesos Espaciales para el Análisis de Redes Sociales
  • Enlaces
  • Resumen
    • español

      El modelado estadístico de redes nos permite caracterizar completamente todo el sistema, así como realizar predicciones sobre la formación de enlaces. Los modelos latentes abordan estas tareas incorporando dependencias no condicionales a través de efectos aleatorios. Un ejemplo notable es el modelo Bayesiano basado en procesos espaciales, que resulta particularmente útil para evitar problemas de sobreajuste que pueden surgir en modelos de espacio de distancia latente. En este trabajo, presentamos la implementación computacional del modelo y evaluamos su bondad de ajuste y desempeño predictivo utilizando redes sintéticas y reales. El modelo demuestra fuertes capacidades para replicar estadísticas de la red y estimar la superficie latente correspondiente. También proponemos un enfoque alternativo de ajuste utilizando un algoritmo de caso-control. Con base en la verosimilitud estimada, el modelo muestra un buen desempeño tanto en términos de predicción como en bondad de ajuste.

    • English

      Statistical modeling of networks enables us to fully characterize the entire system as well as make predictions regarding link formation. Latent models address these tasks by incorporating non-conditional dependencies through random effects. A notable example is the Bayesian spatial process-based model, which is particularly useful for avoiding overfitting issues that may arise in latent distance space models. In this paper, we provide the computational implementation of the model andevaluate its goodness of fit and predictive performance using synthetic networks.The model demonstrates strong capabilities in replicating network statistics andestimating the corresponding latent surface. We also propose an alternative fitting approach using a case-control algorithm. Based on the estimated log-likelihood, the model exhibits good performance in terms of prediction as well as goodness of fit.

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