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Predicting individual socioeconomic status from mobile phone data: a semi-supervised hypergraph-based factor graph approach

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Abstract

Socioeconomic status (SES) is an important economic and social aspect widely concerned. Assessing individual SES can assist related organizations in making a variety of policy decisions. Traditional approach suffers from the extremely high cost in collecting large-scale SES-related survey data. With the ubiquity of smart phones, mobile phone data has become a novel data source for predicting individual SES with low cost. However, the task of predicting individual SES on mobile phone data also proposes some new challenges, including sparse individual records, scarce explicit relationships and limited labeled samples, unconcerned in prior work restricted to regional or household-oriented SES prediction. To address these issues, we propose a semi-supervised hypergraph-based factor graph model (HyperFGM) for individual SES prediction. HyperFGM is able to efficiently capture the associations between SES and individual mobile phone records to handle the individual record sparsity. For the scarce explicit relationships, HyperFGM models implicit high-order relationships among users on the hypergraph structure. Besides, HyperFGM explores the limited labeled data and unlabeled data in a semi-supervised way. Experimental results show that HyperFGM greatly outperforms the baseline methods on a set of anonymized real mobile phone data for individual SES prediction.

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Notes

  1. https://en.wikipedia.org/wiki/Socioeconomic_status.

  2. http://uk.businessinsider.com/.

  3. http://www.esourceresearch.org/portals/0/uploads/documents/public/oakes_fullchapter.pdf.

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Authors and Affiliations

  1. Institute of Computer Science, University of Goettingen, Göttingen, Germany

    Tao Zhao & Xiaoming Fu

  2. School of Computer Science, Huazhong University of Science and Technology, Wuhan, China

    Hong Huang

  3. China TelecomCo., Ltd., Beijing, China

    Xiaoming Yao

  4. Department of Sociology, Tsinghua University, Beijing, China

    Jar-der Luo

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  1. Tao Zhao
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  2. Hong Huang
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  3. Xiaoming Yao
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  4. Jar-der Luo
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  5. Xiaoming Fu
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Correspondence to Tao Zhao.

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This work is partially supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 824019, by the National Natural Science Foundation of China No. 61802140, and by the Hubei Provincial Natural Science Foundation No. 2018CFB200.

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Zhao, T., Huang, H., Yao, X. et al. Predicting individual socioeconomic status from mobile phone data: a semi-supervised hypergraph-based factor graph approach. Int J Data Sci Anal 9, 361–372 (2020). https://doi.org/10.1007/s41060-019-00195-z

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