Joint Modelling of Multiple Network Views

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Title: Joint Modelling of Multiple Network Views
Authors: Gollini, Isabella
Murphy, Thomas Brendan
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Date: 17-Nov-2014
Abstract: 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.
Type of material: Journal Article
Publisher: Taylor and Francis
Copyright (published version): 2014 Taylor and Francis
Keywords: Machine learning;Statistics;Latent space model;Latent variable;Multiplex networks;Social network analysis;Variational methods
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Clique Research Collection
CASL Research Collection
Insight Research Collection

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