  The Infinite Relational Model (IRM) is a probabilistic model for relational
data clustering that partitions objects into clusters based on observed
relationships. This paper presents Averaged CVB (ACVB) solutions for IRM,
convergence-guaranteed and practically useful fast Collapsed Variational Bayes
(CVB) inferences. We first derive ordinary CVB and CVB0 for IRM based on the
lower bound maximization. CVB solutions yield deterministic iterative
procedures for inferring IRM given the truncated number of clusters. Our
proposal includes CVB0 updates of hyperparameters including the concentration
parameter of the Dirichlet Process, which has not been studied in the
literature. To make the CVB more practically useful, we further study the CVB
inference in two aspects. First, we study the convergence issues and develop a
convergence-guaranteed algorithm for any CVB-based inferences called ACVB,
which enables automatic convergence detection and frees non-expert
practitioners from difficult and costly manual monitoring of inference
processes. Second, we present a few techniques for speeding up IRM inferences.
In particular, we describe the linear time inference of CVB0, allowing the IRM
for larger relational data uses. The ACVB solutions of IRM showed comparable or
better performance compared to existing inference methods in experiments, and
provide deterministic, faster, and easier convergence detection.
