Clustering for time-varying relational count data

Satoshi Goto, Mariko Takagishi, Hiroshi Yadohisa

Research output: Contribution to journalArticlepeer-review


Relational count data are often obtained from sources such as simultaneous purchase in online shops and social networking service information. 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 clustering result to understand how objects move between the clusters over time. In this paper, we propose two 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.

Original languageEnglish
Article number107123
JournalComputational Statistics and Data Analysis
Publication statusPublished - Apr 2021
Externally publishedYes


  • Bayesian model
  • Clustering
  • Count data
  • Time-varying relational data
  • Zero-inflated poisson

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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