Variational Bayesian Inference for the Latent Position Cluster Model

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Michael SalterTownshend, Thomas Brendan Murphy
Journal/Conference Name COMPUTATIONAL STATISTICS & DATA ANALYSIS
Paper Category
Paper Abstract A number of recent approaches to modeling social networks have focussed on embedding the nodes in a latent "social space". Nodes that are in close proximity are more likely to form links than those who are distant. This naturally accounts for reciprocal and transitive relationships which are commonly found in many network datasets. The Latent Position Cluster Model is one such model that also explicitly incorporates clustering by modeling the locations using a finite Gaussian mixture model. Observed covariates and sociality random effects may also be modeled. However, inference for the model via MCMC is cumbersome and thus scaling to large networks is a challenge. Variational Bayesian methods offer an alternative inference methodology for this problem. Sampling based MCMC is replaced by an optimization that requires many orders of magnitude fewer iterations to converge. A Variational Bayesian algorithm for the Latent Position Cluster Model is therefore developed and demonstrated.
Date of publication 2013
Code Programming Language R
Comment

Copyright Researcher 2022