CUED Publications database

Dynamic probabilistic models for latent feature propagation in social networks

Heaukulani, C and Ghahramani, Z (2013) Dynamic probabilistic models for latent feature propagation in social networks. 30th International Conference on Machine Learning, ICML 2013. pp. 275-283.

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Abstract

Current Bayesian models for dynamic social network data have focused on modelling the influence of evolving unobserved structure on observed social interactions. However, an understanding of how observed social relationships from the past affect future unobserved structure in the network has been neglected. In this paper, we introduce a new probabilistic model for capturing this phenomenon, which we call latent feature propagation, in social networks. We demonstrate our model's capability for inferring such latent structure in varying types of social network datasets, and experimental studies show this structure achieves higher predictive performance on link prediction and forecasting tasks. Copyright 2013 by the author(s).

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:05
Last Modified: 12 Oct 2017 01:53
DOI: