Gollini, Isabella and Murphy, T.B. (2016) Joint modelling of multiple network wiews. Journal of Computational and Graphical Statistics 25 (1), pp. 246-265. ISSN 1061-8600.
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Abstract
Latent space models (LSM) for network data were introduced by Hoff 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.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | latent space model, latent variable, multiplex networks, social network analysis, variational methods |
| School: | Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School |
| Depositing User: | Isabella Gollini |
| Date Deposited: | 04 Nov 2015 10:23 |
| Last Modified: | 02 Aug 2023 17:19 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/13290 |
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