Variational Bayesian Inference Algorithms for Infinite Relational Model of Network Data

Takuya Konishi, Takatomi Kubo, Kazuho Watanabe, Kazushi Ikeda

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Network data show the relationship among one kind of objects, such as social networks and hyperlinks on the Web. Many statistical models have been proposed for analyzing these data. For modeling cluster structures of networks, the infinite relational model (IRM) was proposed as a Bayesian nonparametric extension of the stochastic block model. In this brief, we derive the inference algorithms for the IRM of network data based on the variational Bayesian (VB) inference methods. After showing the standard VB inference, we derive the collapsed VB (CVB) inference and its variant called the zeroth-order CVB inference. We compared the performances of the inference algorithms using six real network datasets. The CVB inference outperformed the VB inference in most of the datasets, and the differences were especially larger in dense networks.

Original languageEnglish
Article number6937190
Pages (from-to)2176-2181
Number of pages6
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume26
Issue number9
DOIs
Publication statusPublished - Sep 1 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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