A Review of Latent Space Models for Social Networks

• Autores: Juan Sosa, Lina Buitrago
• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 44, Nº. 1, 2021, págs. 171-200
• Idioma: inglés
• DOI: 10.15446/rce.v44n1.89369
• Títulos paralelos:
  • Una revisión de modelos de espacio latente para redes sociales
• Enlaces
  • Texto completo
• Resumen
  • español

    Resumen En este artículo, proporcionamos una revisión sobre los fundamentos de redes sociales y el modelamiento de espacio latente. La primera trata temas importantes relacionados con la descripción de la red, incluidas las características de los vértices y la estructura de la red; mientras que la segunda articula avances relevantes en el modelado de redes, incluidos modelos de grafos aleatorios, modelos de grafos aleatorios generalizados, modelos de grafos aleatorios exponenciales y modelos de espacio social. Discutimos en detalle varios modelos de espacio latente proporcionados en la literatura, prestando especial atención a los modelos de distancia, clase y eigen, en el contexto de redes binarias no dirigidas. Además, también examinamos empíricamente el comportamiento de estos modelos en términos de predicción y bondad de ajuste utilizando más de veinte conjuntos de datos populares de la literatura de redes.

  • English

    Abstract In this paper, we provide a review on both fundamentals of social networks and latent space modeling. The former discusses important topics related to network description, including vertex characteristics and network structure; whereas the latter articulates relevant advances in network modeling, including random graph models, generalized random graph models, exponential random graph models, and social space models. We discuss in detail several latent space models provided in literature, providing special attention to distance, class, and eigen models in the context of undirected, binary networks. In addition, we also examine empirically the behavior of these models in terms of prediction and goodness-of-fit using more than twenty popular datasets of the network literature.

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