Detection of human-interaction network using Markov random field

• Yasuda Muneki

  Graduate School of Science and Engineering, Yamagata University

• Katou Kouta

  Graduate School of Science and Engineering, Yamagata University

• Mikuni Yoshitaka

  Graduate School of System and Information Engineering, University of Tsukuba

• Yokoyama Yuuki

  ALBERT Inc.

• Harada Tomochika

  Graduate School of Science and Engineering, Yamagata University

• Tanaka Atushi

  Graduate School of Science and Engineering, Yamagata University

• Yokoyama Michio

  Graduate School of Science and Engineering, Yamagata University

抄録

Discovering network structures among social actors is one of the most fundamental issues related to social networks. In this paper, we propose a novel and effective algorithm for building a human-interaction network from the location data of individuals gathered by sensors such as the GPS system. We model the problem using Markov random field. The proposed approach combines statistical machine learning with sparse modeling, i.e., the L₁ regularized maximum likelihood approach. We demonstrate the validity of our method through numerical experiments using artificial location data generated from a simulator of quasi-human-transfer.

収録刊行物

• Nonlinear Theory and Its Applications, IEICE

  Nonlinear Theory and Its Applications, IEICE 10 (4), 485-495, 2019

  一般社団法人 電子情報通信学会

キーワード

• social network
• human-interaction network
• graph mining
• Markov random field
• sparse modeling

詳細情報

• CRID: 1390001277363831296
• NII論文ID: 130007722625
• DOI: 10.1587/nolta.10.485
• ISSN: 21854106
• Web Site: https://www.jstage.jst.go.jp/article/nolta/10/4/10_485/_pdf
• 本文言語コード: en
• データソース種別:
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  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可