  Relational count data are often obtained from sources such as simultaneous
purchase in online shops and social networking service information.
Bi-clustering such relational count data reveals the latent structure of the
relationship between objects such as household items or people. When relational
count data observed at multiple time points are available, it is worthwhile
incorporating the time structure into the bi-clustering result to understand
how objects move between the cluster over time. In this paper, we propose two
bi-clustering methods for analyzing time-varying relational count data. The
first model, the dynamic Poisson infinite relational model (dPIRM), handles
time-varying relational count data. In the second model, which we call the
dynamic zero-inflated Poisson infinite relational model, we further extend the
dPIRM so that it can handle zero-inflated data. Proposing both two models is
important as zero-inflated data are often encountered, especially when the time
intervals are short. In addition, by explicitly deriving the relevant full
conditional distributions, we describe the features of the estimated parameters
and, in turn, the relationship between the two models. We show the
effectiveness of both models through a simulation study and a real data
example.
