An Infinite Latent Attribute Model for Network Data

TL;DR

This paper introduces an infinite latent attribute model for network data that captures complex hierarchical structures, improving predictive accuracy over simpler clustering models.

Contribution

The paper proposes a hierarchical Bayesian model with features partitioned into subclusters, capturing more realistic network dependencies than flat or single-layer models.

Findings

Significantly improved link prediction performance

Hierarchical models outperform flat clustering models

Single-layer hierarchies oversimplify real networks

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.