Imputation of missing network data: Some simple procedures

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Journal of Social Structure

International Network for Social Network Analysis

Subject: Social Sciences

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VOLUME 10 , ISSUE 1 (December 2009) > List of articles

Imputation of missing network data: Some simple procedures

Mark Huisman

Keywords : Missingdata; Singleimputation; Descriptivenetworkanalysis; Friendship network

Citation Information : Journal of Social Structure. Volume 10, Issue 1, Pages 1-29, DOI: https://doi.org/10.21307/joss-2019-050

License : (CC BY-NC 4.0)

Published Online: 10-January-2020

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ABSTRACT

Analysis of social network data is often hampered by non-response and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means that the results of social network analyses can be severely biased if missing ties were ignored and only complete cases were analyzed. To overcome the problems created by missing data, several treatment methods are proposed in the literature: model-based methods within the framework of exponential random graph models, and imputation methods. In this paper we focus on the latter group of methods, and investigate the use of some simple imputation procedures to handle missing network data. The results of a simulation study show that ignoring the missing data can have large negative effects on structural properties of the network. Missing data treatment based on simple imputation procedures, however, does also have large negative effects and simple imputations can only successfully correct for non-response in a few specific situations.

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REFERENCES

Barabasi, A-L. and Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.

Boer, P., Huisman, M., Snijders, T.A.B., Steglich, C.E.G., Wichers, L.H.Y., and Zeggelink, E.P.H. (2006). StOCNET: An open software system for the advanced statistical analysis of social networks. Version 1.7. Groningen: ICS / SiencePlus. http://stat.gamma.rug.nl/stocnet/.

Borgatti, S.P. and Molina, J.L. (2003). Ethical and strategic issues in organizational social network analysis. Journal of Applied Behavioral Science, 39, 337–349.

Burt, R.S. (1987a). A note on missing network data in the general social survey. Social Networks, 9, 63–73.

Burt, R.S.(1987b). Socialcontagionandinnovation: Cohesionversusstructuralequivalence. The American Journal of Sociology, 92, 1287–1335.

Butts, C.T. (2003). Network inference, error, and informant (in)accuracy: a Bayesian approach. Social Networks, 25, 103–140.

Costenbader, E. and Valente, T.W. (2003). The stability of centrality measures when networks are sampled. Social Networks, 25, 283–307.

Gabbay, S.M. and Zuckerman, E.W. (1998). Social capital and opportunity in corporate R&D:Thecontingenteffectofcontactdensityonmobilityexpectations. Social Science Research, 27, 189–217.

Ghani, A.C., Donnelly, C.A. and Garnett, G.P. (1998). Sampling biases and missing data in explorationsofsexualpartnernetworksforthespreadofsexuallytransmitteddiseases. Statistics in Medicine, 17, 2079–2097.

Gile, K. and Handcock, M.S. (2006). Model-based assessment of the impact of missing data on inference for networks. CSSS Working paper no. 66, University of Washington, Seattle. (http://www.csss.washington.edu/Papers/wp66.pdf)

Goldstein, J.R. (1999). Kinship networks that cross racial lines: The exception or the rule? Demography, 36, 399–407.

Handcock, M.S. and Gile, K. (2007). Modeling social networks with sampled or missing data. CSSS Working paper no. 75, University of Washington, Seattle. (http://www.csss.washington.edu/Papers/wp75.pdf)

Huisman, M. and Steglich, C.E.G. (2008). Treatment of non-response in longitudinal network studies. Social Networks, 30, 297–308.

Huisman, M. and van Duijn, M.A.J. (2005). Software for social network analysis. In Carrington, P.J., Scott, J., and Wasserman, S. (Eds.), Models and Methods in Social Network Analysis, pp. 270–316. Cambridge University Press, Cambridge.

Kossinets, G. (2006). Effects of missing data in social networks. Social Networks, 28, 247– 268.

Koskinen, J. (2007). Fitting models to social networks with missing data. Paper presented at Sunbelt XXVII, the International Sunbelt Social Network Conference, May 1–6, 2007, Corfu, Greece.

Little, R.A.J. and Rubin, D.B. (1987). Statistical Analysis with Missing Data. New York: Wiley.

McKnight, P.E., McKnight, K.M., Sidani, S., and Figueredo, A.J. (2007). Missing Data. A Gentle Introduction. New York: Guildford Press.

Newman, M.E.J. (2003). Mixing patterns in networks. Physical Review E, 67, 026126.

Newman, M.E.J., Strogatz, S.H., and Watts, D.J. (2001). Random graphs with arbitrary degree distributions and their applications. Physical Review E, 64, 026118.

Pearson, M. and West, P. (2003). Drifting smoke rings: social network analysis and Markov processes in a longitudinal study of friendship groups and risk-taking. Connections, 25, 59–76.

Robins, G., Pattison, P., and Woolcock, J. (2004). Missing data in networks: exponential random graph (p∗) models for networks with non-respondents. Social Networks, 26, 257–283.

Rubin, D.B. (1976). Inference and missing data. Biometrika, 63, 581–592.

Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.

Sande, I.G. (1982). Imputation in surveys: Coping with reality. The American Statistician, 36, 145–152.

Schafer, J.L. and Graham, J.W. (2002). Missing data: our view of the state of the art. Psychological Methods, 7, 147–177.

Snijders, T.A.B. (2005). Models for longitudinal network data. In Carrington, P.J., Scott, J., and Wasserman, S. (Eds.), Models and Methods in Social Network Analysis, pp. 215–247. Cambridge University Press, Cambridge.

Steglich, C.E.G., Snijders, T.A.B., and West, P. (2006). Applying SIENA: An illustrative analysis of the co-evolution of adolescents’ friendship networks, taste in music, and alcohol consumption. Methodology, 2, 48–56.

Steinley, D. and Wasserman, S. (2006). Approximate distributions of several common graph statistics: hypothesis testing applied to a terrorist network. Proceedings of the American Statistical Association, Statistical Applications in Defense and National Security. Santa Monica, CA: Rand Corporation.

Stork, D. and Richards, W.D. (1992). Nonrespondents in communication network studies. Group & Organization Management, 17, 193–209.

Van de Bunt, G.G. (1999). Friends by choice. An actor-oriented statistical network model for friendship networks through time. Amsterdam: Thesis Publishers.

Ward, M.D., Hoff, P.D., and Lofdahl, C.L. (2003). Identifying international networks: Latent spaces and imputation. In Breiger, R., Carley, k., and Pattison, P. (Eds.), Dynamic Social Network Modelling and Analysis: Workshop Summary and Papers, pp. 345–360. Washington: The National Academic Press.

Wasserman, S. and Faust, K. (1994). Social Network Analysis. Methods and applications. Cambridge: Cambridge University Press.

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