Variational Bayesian Inference Algorithms for Infinite Relational Model of Network Data

Takuya Konishi, Takatomi Kubo, Kazuho Watanabe, Kazushi Ikeda

研究成果: Contribution to journalArticle査読

6 被引用数 (Scopus)

抄録

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.

本文言語英語
論文番号6937190
ページ(範囲)2176-2181
ページ数6
ジャーナルIEEE Transactions on Neural Networks and Learning Systems
26
9
DOI
出版ステータス出版済み - 9 1 2015
外部発表はい

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信
  • 人工知能

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