Joint Modeling of Multiple Network Views

• Activity Number: 49
• Type: Invited
• Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Thomas Brendan Murphy*+ and Isabella Gollini
• Companies: University College Dubln and National University of Ireland Maynooth
• Keywords: Network ; Bayesian ; Latent Variable ; Variational ; Latent Space Model ; Social Network

Abstract

Latent space models (LSM) for network data were introduced by Ho? et al. (2002) under the basic assumption that each node of the network has an unknown position in a D-dimensional Euclidean space: generally the smaller the distance between two nodes in the latent space, the greater their probability of being connected.

In many cases, di?erent 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: the 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.