An Infinite Latent Attribute Model for Network Data
This addresses the oversimplification of real networks in existing models, benefiting researchers in network analysis and machine learning, though it is incremental as it builds on hierarchical Bayesian approaches.
The authors tackled the problem of modeling complex network structures by proposing an infinite latent attribute model with a second layer of hierarchy, achieving significantly improved predictive performance on social and biological link prediction tasks.
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.