2019 Volume 10 Issue 4 Pages 485-495
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.