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How Social Networks Influence Human Behavior: An Integrated Latent Space Approach for Differential Social Influence

Published online by Cambridge University Press:  01 January 2025

Jina Park ,
Ick Hoon Jin Open the ORCID record for Ick Hoon Jin [Opens in a new window]  and
Minjeong Jeon
Jina Park
Affiliation:
Yonsei University
Ick Hoon Jin*
Affiliation:
Yonsei University
Minjeong Jeon
Affiliation:
University of California
*
Correspondence should be made to Ick Hoon Jin, Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea. Email: ijin@yonsei.ac.kr
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Abstract

How social networks influence human behavior has been an interesting topic in applied research. Existing methods often utilized scale-level behavioral data (e.g., total number of positive responses) to estimate the influence of a social network on human behavior. This study proposes a novel approach to studying social influence that utilizes item-level behavioral measures. Under the latent space modeling framework, we integrate the two latent spaces for respondents’ social network data and item-level behavior measures into a single space we call ‘interaction map’. The interaction map visualizes the association between the latent homophily among respondents and their item-level behaviors, revealing differential social influence effects across item-level behaviors. We also measure overall social influence by assessing the impact of the interaction map. We evaluate the properties of the proposed approach via extensive simulation studies and demonstrate the proposed approach with a real data in the context of studying how students’ friendship network influences their participation in school activities.

Keywords

differential social influencenetwork analysisitem response modellatent space modellatent space item response modelpeer Network
Type
Theory and Methods
Information
Psychometrika , Volume 88 , Issue 4 , December 2023 , pp. 1529 - 1555
Copyright
Copyright © 2023 The Author(s) under exclusive licence to The Psychometric Society

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Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11336-023-09934-5.

References

References

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Daraganova, G., & Robins, G. (2013). Autologistic actor attribute models. Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, pp. 102–114Google Scholar
Decelle, A., Krzakala, F., Moore, C., & Zdeborová, L. (2011). Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications. Physical Review E, 84(6 CrossRefGoogle ScholarPubMed
Dittrich, D., Leenders, R. T. A. J., & Mulder, J. (2019). Network autocorrelation modeling: A bayes factor approach for testing (multiple) precise and interval hypotheses. Sociological Methods & Research, 48(3), 642–676.CrossRefGoogle Scholar
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Erdős, P., & Rényi, A. (1960). On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17–61.Google Scholar
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Fienberg, S. E. (2012). A brief history of statistical models for network analysis and open challenges. Journal of Computational and Graphical Statistics, 21(4), 825–839.CrossRefGoogle Scholar
Fosdick, B. K., & Hoff, P. D. (2015). Testing and modeling dependencies between a network and nodal attributes. Journal of the American Statistical Association, 110(511), 1047–1056.CrossRefGoogle ScholarPubMed
Frank, K. A., & Xu, R. (2021). Causal inference for social network analysis. The oxford handbook of social networksOxford University Press.Google Scholar
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Fujimoto, K., Wang, P., & Valente, T. W. (2013). The decomposed affiliation exposure model: A network approach to segregating peer influences from crowds and organized sports. Network Science, 1(2), 154–169.CrossRefGoogle Scholar
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Gollini, I., & Murphy, T. B. (2016). Joint modeling of multiple network views. Journal of Computational and Graphical Statistics, 25(1), 246–265.CrossRefGoogle Scholar
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Jeon, M., Jin, I. H., Schweinberger, M., & Baugh, S. (2021). Mapping unobserved item-respondent interactions: A latent space item response model with interaction map. Psychometrika, 86(2), 378–403.CrossRefGoogle ScholarPubMed
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Krivitsky, P. N., Handcock, M. S., Raftery, A. E., & Hoff, P. D. (2009). Representing degree distributions, clustering, and homophily in social networks with latent cluster random network models. Social Networks, 31, 204–213.CrossRefGoogle Scholar
Larson, R., Hansen, D., & Moneta, G. (2006). Differing profiles of developmental experiences across types of organized youth activities. Developmental Psychology, 42(5), 849–863.CrossRefGoogle ScholarPubMed
Lauritzen, S., Rinaldo, A., & Sadeghi, K. (2017). Random networks, graphical models, and exchangeability. Journal of the Royal Statistical Society, Series B, 80(3), 481–508.CrossRefGoogle Scholar
Lee, C., & Wilkinson, D. J. (2019). A review of stochastic block models and extensions for graph clustering. Applied Network Science, 4 122.CrossRefGoogle Scholar
Leenders, R. T. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47.CrossRefGoogle Scholar
Lei, J., & Rinaldo, A. (2015). Consistency of spectral clustering in stochastic block models. The Annals of Statistics, 43(1), 215–237.CrossRefGoogle Scholar
Lu, X., & Szymanski, B. K. (2019). A regularized stochastic block model for the robust community detection in complex networks. Scientific Reports, 9 13247.CrossRefGoogle ScholarPubMed
Mahoney, J. L., Cairns, B. D., & Farmer, T. W. (2003). Promoting interpersonal competence and educational success through extracurricular activity participation. Journal of Educational Psychology, 95(2), 409–418.CrossRefGoogle Scholar
Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. The Review of Economic Studies, 60(3), 531.CrossRefGoogle Scholar
Matias, N. C. F. (2019). Elaboración de una escala de participación en actividades extracurriculares para niños. Ciencias Psicológicas, 235–248.CrossRefGoogle Scholar
McCabe, K., Modecki, K., & Barber, B. (2016). Participation in organized activities protects against adolescents’ risky substance use, even beyond development in conscientiousness. Journal of Youth and Adolescence, 45(11), 2292–2306.CrossRefGoogle ScholarPubMed
Mercken, L., Snijders, T. A., Steglich, C., Vertiainen, E., & Vries, H. D. (2010). Smoking-based selection and influence in gender-segregated friendship networks: A social network analysis of adolescent smoking. Addiction, 105(7), 1280–1289.CrossRefGoogle ScholarPubMed
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Paluck, E. L., Shepherd, H., & Aronow, P. M. (2016). Changing climates of conflict: A social network experiment in 56 schools. Proceedings of the National Academy of Sciences of the United States of America, 113(3), 566–571.CrossRefGoogle ScholarPubMed
Paluck, E. L., Shepherd, H., & Aronow, P. M. (2020). Changing climates of conflict: A social network experiment in 56 schools, new jersey, 2012–2013. Inter-university Consortium for Political and Social Research.Google Scholar
Parker, A., Pallotti, F., & Lomi, A. (2021). New network models for the analysis of social contagion in organizations: An introduction to autologistic actor attribute models. Organizational Research Methods, 25(3), 513–540.CrossRefGoogle Scholar
Raftery, A., Niu, X., Hoff, P., & Yeung, K. (2012). Fast inference for the latent space network model using a case-control approximate likelihood. Journal of Computational and Graphical Statistics, 21(4), 909–919.CrossRefGoogle ScholarPubMed
Rastelli, R., Friel, N., & Raftery, A. (2016). Properties of latent variable network models. Network Science, 4, 407–432.CrossRefGoogle Scholar
Ripley, R. M., Snijders, T. A. B., B’oda, Z., V"or"os, A., & Preciado, P. (2022). Manual for Siena version 4.0. Technical report, Oxford: University of Oxford, Department of Statistics; Nuffield College. R package version 1.3.14. https://www.cran.r-project.org/web/packages/RSiena/.Google Scholar
Robins, G., Pattison, P., & Elliott, P. (2001). Network models for social influence processes. Psychometrika, 66(2), 161–189.CrossRefGoogle Scholar
Robins, G., Snijders, T., Wang, P., Handcock, M., & Pattison, P. (2007). Recent developments in exponential random graph (p*) models for social networks. Social Networks, 29(2), 192–215.CrossRefGoogle Scholar
Rohe, K., Chatterjee, S., & Yu, B. (2011). Spectral clustering and the high-dimensional stochastic blockmodel. The Annals of Statistics, 39(4), 1878–1915.CrossRefGoogle Scholar
Salter-Townshend, M., & McCormick, T. H. (2017). Latent space models for multiview network data. The Annals of Applied Statistics, 11(3), 1217–1244.CrossRefGoogle ScholarPubMed
Scott, D., Dam, I., & Wilton, R. (2012). Investigating the effects of social influence on the choice to telework. Environment and Planning A, 44(5), 1016–1031.CrossRefGoogle Scholar
Sewell, D. K. (2017). Network autocorrelation models with egocentric data. Social Networks, 49, 113–123.CrossRefGoogle Scholar
Sewell, D. K., & Chen, Y. (2015). Latent space models for dynamic networks. Journal of the American Statistical Association, 110(512), 1646–1657.CrossRefGoogle Scholar
Shakarian, P., Bhatnagar, A., Aleali, A., Shaabani, E., & Guo, R. (2015). The Independent Cascade and Linear Threshold Models (pp. 35–48). Springer.Google Scholar
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