Hierarchical Infinite Relational Model
This addresses the problem of analyzing complex relational data for researchers in fields like politics and genomics, though it is incremental as it builds on existing relational models.
The paper introduces the hierarchical infinite relational model (HIRM), a probabilistic generative model for noisy, sparse, and heterogeneous relational data, which generalizes the standard infinite relational model and is applied to tasks like density estimation and structure discovery, achieving results on datasets with up to 18 million cells.
This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data. Given a set of relations defined over a collection of domains, the model first infers multiple non-overlapping clusters of relations using a top-level Chinese restaurant process. Within each cluster of relations, a Dirichlet process mixture is then used to partition the domain entities and model the probability distribution of relation values. The HIRM generalizes the standard infinite relational model and can be used for a variety of data analysis tasks including dependence detection, clustering, and density estimation. We present new algorithms for fully Bayesian posterior inference via Gibbs sampling. We illustrate the efficacy of the method on a density estimation benchmark of twenty object-attribute datasets with up to 18 million cells and use it to discover relational structure in real-world datasets from politics and genomics.