Now showing 1 - 2 of 2
  • Publication
    Joint Modelling of Multiple Network Views
    (Taylor and Francis, 2014-11-17) ;
    Latent space models (LSM) for network data were introduced by Holf et al. (2002) under the basic assumption that each node of the network has an unknown position in a D-dimensional Euclidean latent space: generally the smaller the distance between two nodes in the latent space, the greater their probability of being connected. In this paper we propose a variational inference approach to estimate the intractable posterior of the LSM. In many cases, different network views on the same set of nodes are available. It can therefore be useful to build a model able to jointly summarise the information given by all the network views. For this purpose, we introduce the latent space joint model (LSJM) that merges the information given by multiple network views assuming that the probability of a node being connected with other nodes in each network view is explained by a unique latent variable. This model is demonstrated on the analysis of two datasets: an excerpt of 50 girls from 'Teenage Friends and Lifestyle Study' data at three time points and the Saccharomyces cerevisiae genetic and physical protein-protein interactions.
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  • Publication
    Review of Statistical Network Analysis: Models, Algorithms, and Software
    The analysis of network data is an area that is rapidly growing, both within and outside of the discipline of statistics. This review provides a concise summary of methods and models used in the statistical analysis of network data, including the Erdos–Renyi model, the exponential family class of network models, and recently developed latent variable models. Many of the methods and models are illustrated by application to the well-known Zachary karate dataset. Software routines available for implementing methods are emphasized throughout. The aim of this paper is to provide a review with enough detail about many common classes of network models to whet the appetite and to point the way to further reading.
    Scopus© Citations 83  9441