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Dynamic network prediction

Published online by Cambridge University Press:  09 July 2020

Ravi Goyal Open the ORCID record for Ravi Goyal [Opens in a new window]  and
Victor De Gruttola
Ravi Goyal*
Affiliation:
Mathematica
Victor De Gruttola
Affiliation:
Department of Biostatistics, Harvard School of Public Health (e-mail: degrut@hsph.harvard.edu)
*
*Corresponding author. Email: ravi.j.goyal@gmail.com
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Abstract

We present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the U.S. Senate from 2003 to 2016.

Keywords

dynamic networkcongruence class modelpredictionco-sponsorship network

Information

Type
Research Article
Information
Network Science , Volume 8 , Issue 4 , December 2020 , pp. 574 - 595
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

Action Editor: Stanley Wasserman

References

Airoldi, E. M., Bai, X., & Carley, K. M. (2011). Network sampling and classification: An investigation of network model representations. Decision Support Systems, 51(3), 506–518.CrossRefGoogle ScholarPubMed
Carson, J. L., Crespin, M. H., Finocchiaro, C. J., & Rohde, D. W. (2007). Redistricting and party polarization in the us house of representatives. American Politics Research, 35(6), 878–904.CrossRefGoogle Scholar
Chatterjee, S., et al. (2015). Matrix estimation by universal singular value thresholding. The Annals of Statistics, 43(1), 177–214.CrossRefGoogle Scholar
Enten, H. (2018). Ending gerrymandering won’t fix what ails america. [Online; posted 26-January-2018].Google Scholar
Erdös, 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
Fowler, J. H. (2006). Connecting the congress: A study of cosponsorship networks. Political Analysis, 14(4), 456–487.CrossRefGoogle Scholar
Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Sssociation, 81, 832–842.CrossRefGoogle Scholar
Goyal, R., Blitzstein, J., & Gruttola, V. D. (2014). Sampling networks from their posterior predictive distribution. Network Science, 2(1), 107–131.CrossRefGoogle ScholarPubMed
Goyal, R., & De Gruttola, V. (2015). Sampling dynamic networks with application to investigation of HIV epidemic drivers. Mathematical Biosciences, 267, 124–133.CrossRefGoogle ScholarPubMed
Goyal, R., & De Gruttola, V. (2017). Inference on network statistics by restricting to the network space: Applications to sexual history data. Statistics in Medicine, 37(2), 218–235.CrossRefGoogle Scholar
Goyal, R., Wang, R., & DeGruttola, V. (2012). Editorial commentary: Network epidemic models: Assumptions and interpretations. Clinical Infectious Diseases, 55(2), 276–278.CrossRefGoogle ScholarPubMed
Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.CrossRefGoogle Scholar
Hanneke, S., & Xing, E. P. (2007). Discrete temporal models of social networks. In Statistical network analysis: Models, issues, and new directions (pp. 115–125). Springer.Google 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).CrossRefGoogle Scholar
Hyndman, R. J. (2013). forecast: Forecasting functions for time series and linear models. R package version 4.8.Google Scholar
Jacobson, G. C. (2016). Polarization, gridlock, and presidential campaign politics in 2016. The Annals of the American Academy of Political and Social Science, 667(1), 226–246.CrossRefGoogle Scholar
Kirkland, J. H. (2011). The relational determinants of legislative outcomes: Strong and weak ties between legislators. The Journal of Politics, 73(3), 887–898.CrossRefGoogle Scholar
Kirkland, J. H, & Gross, J. H. (2014). Measurement and theory in legislative networks: The evolving topology of congressional collaboration. Social Networks, 36, 97–109.CrossRefGoogle Scholar
Krivitsky, P. N., & Handcock, M. S. (2013). A separable model for dynamic networks. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(1), 29–46.CrossRefGoogle Scholar
Krivitsky, P. N., & Handcock, M. S. (2019). tergm: Fit, simulate and diagnose models for network evolution based on exponential-family random graph models. The Statnet Project. R package version 3.6.1.Google Scholar
Newman, M. E. (2010). Networks an introduction. New York: Oxford University Press.CrossRefGoogle Scholar
Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015). Epidemic processes in complex networks. Reviews of Modern Physics, 87(3), 925.CrossRefGoogle Scholar
Paul, S., & O’Malley, A. J. (2013). Hierarchical longitudinal models of relationships in social networks. Journal of the Royal Statistical Society: Series C (Applied Statistics).Google Scholar
Pellis, L., Ball, F., Bansal, S., Eames, K., House, T., Isham, V., & Trapman, P. (2015). Eight challenges for network epidemic models. Epidemics, 10, 58–62.CrossRefGoogle ScholarPubMed
Schweinberger, M. (2012). Statistical modelling of network panel data: Goodness of fit. British Journal of Mathematical and Statistical Psychology, 65(2), 263–281.CrossRefGoogle ScholarPubMed
Sewell, D. K., & Chen, Y. (2015). Latent space models for dynamic networks. Journal of the American Statistical Association, 110(512), 1646–1657.CrossRefGoogle Scholar
Snijders, T. A. B. (1996). Stochastic actor-oriented models for network change. Journal of Mathematical Sociology, 21(1–2), 149–172.CrossRefGoogle Scholar
Snijders, T. A. B. (2017). Stochastic actor-oriented models for network dynamics. Annual Review of Statistics and Its Application, 4, 343–363.Google Scholar
Stadtfeld, C. (2018). The micro–macro link in social networks. Emerging Trends in the Social and Behavioral Sciences, 1–15.Google Scholar
Tam Cho, W. K., & Fowler, J. H. (2010). Legislative success in a small world: Social network analysis and the dynamics of congressional legislation. The Journal of Politics, 72(1), 124–135.CrossRefGoogle Scholar
Wang, R., Goyal, R., Lei, Q., Essex, M., & Gruttola, V. D. (2014). Sample size considerations in the design of cluster randomized trials of combination HIV prevention. Clinical Trials, 11(3), 309–318CrossRefGoogle ScholarPubMed
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Internet Mathematics, 393(6684), 397–498.Google ScholarPubMed
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