Palla, K and Knowles, DA and Ghahramani, Z (2015) *Relational Learning and Network Modelling Using Infinite Latent Attribute Models.* IEEE Trans Pattern Anal Mach Intell, 37. pp. 462-474.

## Abstract

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

Item Type: | Article |
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Uncontrolled Keywords: | Computer Simulation Informatics Machine Learning Models, Theoretical |

Subjects: | UNSPECIFIED |

Divisions: | Div F > Computational and Biological Learning |

Depositing User: | Cron Job |

Date Deposited: | 17 Jul 2017 19:45 |

Last Modified: | 21 Sep 2017 01:35 |

DOI: |