Detection of human-interaction network using Markov random field

Muneki Yasuda; Kouta Katou; Yoshitaka Mikuni; Yuuki Yokoyama; Tomochika Harada; Atushi Tanaka; Michio Yokoyama

doi:10.1587/nolta.10.485

Special Issue on Recent Progress in Nonlinear Theory and Its Applications

Detection of human-interaction network using Markov random field

Muneki Yasuda, Kouta Katou, Yoshitaka Mikuni, Yuuki Yokoyama, Tomochika Harada, Atushi Tanaka, Michio Yokoyama

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Keywords: social network, human-interaction network, graph mining, Markov random field, sparse modeling

JOURNAL FREE ACCESS

2019 Volume 10 Issue 4 Pages 485-495

DOI https://doi.org/10.1587/nolta.10.485

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Abstract

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.

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