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Latent space models for network perception data

Published online by Cambridge University Press:  15 April 2019

Daniel K. Sewell Open the ORCID record for Daniel K. Sewell [Opens in a new window]
Daniel K. Sewell*
Affiliation:
Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA
*
Corresponding author. Email: daniel-sewell@uiowa.edu
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Abstract

Social networks, wherein the edges represent nonbehavioral relations such as friendship, power, and influence, can be difficult to measure and model. A powerful tool to address this is cognitive social structures (Krackhardt, D. (1987). Cognitive social structures. Social Networks, 9(2), 109–134.), where the perception of the entire network is elicited from each actor. We provide a formal statistical framework to analyze informants’ perceptions of the network, implementing a latent space network model that can estimate, e.g., homophilic effects while accounting for informant error. Our model allows researchers to better understand why respondents’ perceptions differ. We also describe how to construct a meaningful single aggregated network that ameliorates potential respondent error. The proposed method provides a visualization method, an estimate of the informants’ biases and variances, and we describe a method for sidestepping forced-choice designs.

Keywords

cognitive social structureslatent space network modelnetwork estimationsocial network analysisvisualization
Type
Original Article
Information
Network Science , Volume 7 , Issue 2 , June 2019 , pp. 160 - 179
Copyright
© Cambridge University Press 2019 

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References

Aarstad, J., Selart, M., & Troye, S. (2011). Advice seeking network structures and the learning organization. Problems and Perspectives in Management, 9(2), 63–70.Google Scholar
Adams, J., & Moody, J. (2007). To tell the truth: Measuring concordance in multiply reported network data. Social Networks, 29(1), 44–58.CrossRefGoogle Scholar
Almquist, Z. W. (2012). Random errors in egocentric networks. Social Networks, 34(4), 493–505.CrossRefGoogle ScholarPubMed
An, W., & Schramski, S. (2015). Analysis of contested reports in exchange networks based on actors credibility. Social Networks, 40, 25–33.CrossRefGoogle Scholar
Austin, A., Linkletter, C., & Wu, Z. (2013). Covariate-defined latent space random effects model. Social Networks, 35(3), 338–346.CrossRefGoogle Scholar
Batchelder, E. (2002). Comparing three simultaneous measurements of a sociocognitive network. Social Networks, 24(3), 261–277.CrossRefGoogle Scholar
Batchelder, W. H., & Romney, A. K. (1988). Test theory without an answer key. Psychometrika, 53(1), 71–92.CrossRefGoogle Scholar
Batchelder, W. H., & Romney, A. K. (1989). New results in test theory without an answer key (pp. 229–248). Berlin, Heidelberg: Springer.Google Scholar
Batchelder, W. H., Kumbasar, E., & Boyd, J. P. (1997). Consensus analysis of three-way social network data. Journal of Mathematical Sociology, 22(1), 29–58.CrossRefGoogle Scholar
Bell, D. C., Montoya, L. D., & Atkinson, J. S. (2000). Partner concordance in reports of joint risk behaviors. Journal of Acquired Immune Deficiency Syndromes, 25, 173–181.CrossRefGoogle ScholarPubMed
Bernard, H. R., Killworth, P. D., & Sailer, L. (1979). Informant accuracy in social network data IV: A comparison of clique-level structure in behavioral and cognitive network data. Social Networks, 2(3), 191–218.CrossRefGoogle Scholar
Bernard, H. R., Killworth, P. D., & Sailer, L. (1982). Informant accuracy in social-network data V: An experimental attempt to predict actual communication from recall data. Social Science Research, 11(1), 30–66.CrossRefGoogle Scholar
Bond, C. F. Jr., Horn, E. M., & Kenny, D. A. (1997). A model for triadic relations. Psychological Methods, 2(1), 79–94.CrossRefGoogle Scholar
Bondonio, D. (1998). Predictors of accuracy in perceiving informal social networks. Social Networks, 20(4), 301–330.CrossRefGoogle Scholar
Brands, R. A. (2013). Cognitive social structures in social network research: A review. Journal of Organizational Behavior, 34, S82–S103.CrossRefGoogle Scholar
Brewer, D. D. (2000). Forgetting in the recall-based elicitation of personal and social networks. Social Networks, 22(1), 29–43.CrossRefGoogle Scholar
Brewer, D. D., & Webster, C. M. (2000). Forgetting of friends and its effects on measuring friendship networks. Social Networks, 21(4), 361–373.CrossRefGoogle Scholar
Butts, C. T. (2003). Network inference, error, and informant (in)accuracy: A Bayesian approach. Social Networks, 25(2), 103–140.CrossRefGoogle Scholar
Butts, C. T. (2008). A relational event framework for social action. Sociological Methodology, 38, 155–200.CrossRefGoogle Scholar
Butts, C. T. (2011). Bayesian meta-analysis of social network data via conditional uniform graph quantiles. Sociological Methodology, 41, 257–298.CrossRefGoogle Scholar
Casciaro, T. (1998). Seeing things clearly: Social structure, personality, and accuracy in social network perception. Social Networks, 20(4), 331–351.CrossRefGoogle Scholar
Casciaro, T., Carley, K. M., & Krackhardt, D. (1999). Positive affectivity and accuracy in social network perception. Motivation and Emotion, 23(4), 285–306.CrossRefGoogle Scholar
Davis, J. A., & Leinhardt, S. (1972). Sociological theories in progress. In The structure of positive interpersonal relations in small groups (vol. 2). Boston: Houghton Mifflin.Google Scholar
Durante, D., & Dunson, D. B. (2014). Nonparametric Bayes dynamic modelling of relational data. Biometrika, 101(4), 883–898.CrossRefGoogle Scholar
Durante, D., Dunson, D. B, & Vogelstein, J. T. (2017). Nonparametric Bayes modeling of populations of networks. Journal of the American Statistical Association, 112(520), 1516–1530.CrossRefGoogle Scholar
Freeman, L., & Romney, A. K. (1987). Words, deeds and social structure: A preliminary study of the reliability of informants. Human Organization, 46(4), 330–334.CrossRefGoogle Scholar
Freeman, L. C., Romney, A. K., & Freeman, S. C. (1987). Cognitive structure and informant accuracy. American Anthropologist, 89(2), 310–325.CrossRefGoogle Scholar
Geweke, J. (1992). Bayesian statistics 4. In Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. Oxford: Clarendon Press.Google Scholar
Grippa, F., & Gloor, P. A. (2009). You are who remembers you. detecting leadership through accuracy of recall. Social Networks, 31(4), 255–261.CrossRefGoogle Scholar
Hammer, M. (1985). Implications of behavioral and cognitive reciprocity in social network data. Social Networks, 7(2), 189–201.CrossRefGoogle Scholar
Handcock, M. S, Raftery, A. E, & Tantrum, J. M. (2007). Model-based clustering for social networks. Journal of the Royal Statistical Society, Series A, 170(2), 301–354.CrossRefGoogle Scholar
Hildum, D. C. (1986). ‘Competence’ and ‘performance’ in network structure. Social Networks, 8(1), 79–95.CrossRefGoogle Scholar
Hoff, P. D. (2005). Bilinear mixed-effects models for dyadic data. Journal of the American Statistical Association, 100(469), 286–295.CrossRefGoogle Scholar
Hoff, P. D. (2009). Multiplicative latent factor models for description and prediction of social networks. Computational and Mathematical Organization Theory, 15(4), 261–272.CrossRefGoogle Scholar
Hoff, P. D, Raftery, A. E, & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098.CrossRefGoogle Scholar
Hunter, D. R., Goodreau, S. M., & Handcock, M. S. (2008). Goodness of fit of social network models. Journal of the American Statistical Association, 103(481), 248–258.CrossRefGoogle Scholar
Johnson, J. C., & Orbach, M. K. (2002). Perceiving the political landscape: Ego biases in cognitive political networks. Social Networks, 24(3), 291–310.CrossRefGoogle Scholar
Kenny, D. A. (1994). Interpersonal perception: A social relations analysis. New York: Guilford Press.Google ScholarPubMed
Kilduff, M., Crossland, C., Tsai, W., & Krackhardt, D. (2008). Organizational network perceptions versus reality: A small world after all? Organizational Behavior and Human Decision Processes, 107(1), 15–28.CrossRefGoogle Scholar
Killworth, P. D., & Bernard, H. R. (1976). Informant accuracy in social network data. Human Organization, 35(3), 269–286.CrossRefGoogle Scholar
Killworth, P. D., & Bernard, H. R. (1977). Informant accuracy in social network data II. Human Communication Research, 4(1), 3–18.Google Scholar
Killworth, P. D., & Bernard, H. R. (1979). Informant accuracy in social network data III: A comparison of triadic structure in behavioral and cognitive data. Social Networks, 2(1), 19–46.CrossRefGoogle Scholar
Koskinen, J. H. (2002a). Bayesian analysis of cognitive social structures with covariates (Working Paper 2002:3). Department of Statistics.Google Scholar
Koskinen, J. H. (2002b). Bayesian analysis of perceived social networks (Working Paper 2002:3). Department of Statistics.Google Scholar
Koskinen, J. H. (2004). Model selection for cognitive social structures (Working Paper 2004:3). Department of Statistics.Google Scholar
Krackhardt, D. (1987). Cognitive social structures. Social Networks, 9(2), 109–134.CrossRefGoogle Scholar
Krackhardt, D. (1990). Assessing the political landscape: Structure, cognition, and power in organizations. Administrative Science Quarterly, 35(2), 342–369.CrossRefGoogle Scholar
Krackhardt, D., & Kilduff, M. (1999). Whether close or far: Social distance effects on perceived balance in friendship networks. Journal of Personality and Social Psychology, 76(5), 770–782.CrossRefGoogle Scholar
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 effects models. Social Networks, 31(3), 204–213.CrossRefGoogle ScholarPubMed
Kumbasar, E., Rommey, A. K., & Batchelder, W. H. (1994). Systematic biases in social perception. American Journal of Sociology, 100(2), 477–505.CrossRefGoogle Scholar
Lorant, V., Soto, V. E., Alves, J., Federico, B., Kinnunen, J., Kuipers, M., ... Kunst, A. (2015). Smoking in school-aged adolescents: Design of a social network survey in six european countries. BMC Research Notes, 8, 91.CrossRefGoogle ScholarPubMed
Nakao, K., & Romney, A. K. (1993). Longitudinal approach to subgroup formation: Re-analysis of newcomb’s fraternity data. Social Networks, 15(2), 109–131.CrossRefGoogle Scholar
Neal, J. W., Neal, Z. P., & Cappella, E. (2014). I know who my friends are, but do you? Predictors of self-reported and peer-inferred relationships. Child Development, 85(4), 1366–1372.CrossRefGoogle Scholar
Neal, J. W., Neal, Z. P., & Cappella, E. (2016). Seeing and being seen: Predictors of accurate perceptions about classmates relationships. Social Networks, 44, 1–8.CrossRefGoogle ScholarPubMed
Pattison, P. (1994). Social cognition in context: Some applications of social network analysis. (Chap. 4, pp. 79–109). Advances in Social Network Analysis. Thousand Oaks, USA: Sage.Google Scholar
Perry, B. L., & Pescosolido, B. A. (2015). Social network activation: The role of health discussion partners in recovery from mental illness. Social Science & Medicine, 125, 116–128.CrossRefGoogle ScholarPubMed
Romney, A. K., & Faust, K. (1982). Predicting the structure of a communications network from recalled data. Social Networks, 4, 285–304.CrossRefGoogle Scholar
Romney, A. K., Brewer, D. D., & Batchelder, W. H. (1996). The relation between typicality and semantic similarity structure. Journal of Quantitative Anthropology, 6(1–2), 1–14.Google 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
Sewell, D. K, & Chen, Y. (2016). Latent space models for dynamic networks with weighted edges. Social Networks, 44, 105–116.CrossRefGoogle Scholar
Sewell, D. K, & Chen, Y. (2017). Latent space approaches to community detection in dynamic networks. Bayesian Analysis, 12(2), 351–377.CrossRefGoogle Scholar
Shakya, H. B., Christakis, N. A., & Fowler, J. H. (2015). Social network predictors of latrine ownership. Social Science & Medicine, 125, 129–138.CrossRefGoogle ScholarPubMed
Shoham, D. A., Harris, J. K., Mundt, M., & McGaghie, W. (2016). A network model of communication in an interprofessional team of healthcare professionals: A cross-sectional study of a burn unit. Journal of Interprofessional Care, 30(5), 661–667.CrossRefGoogle Scholar
Siciliano, M. D, Yenigun, D., & Ertan, G. (2012). Estimating network structure via random sampling: Cognitive social structures and the adaptive threshold method. Social Networks, 34(4), 585–600.CrossRefGoogle Scholar
Sosa, J., & Rodriguez, A. (2018+). A latent space model for cognitive social structures data. arXiv:1711.03662.Google Scholar
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B, 64(4), 583–639.CrossRefGoogle Scholar
Swartz, T. B, Gill, P. S, & Muthukumarana, S. (2015). A Bayesian approach for the analysis of triadic data in cognitive social structures. Journal of the Royal Statistical Society, Series C, 64(4), 593–610.CrossRefGoogle Scholar
Warner, R. M, Kenny, D. A, & Stoto, M. (1979). A new round robin analysis of variance for social interaction data. Journal of Personality and Social Psychology, 37(10), 1742–1757.CrossRefGoogle Scholar
Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. New York: Cambridge University Press.CrossRefGoogle Scholar
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