An Approximation Method for Improving Dynamic Network Model Fitting

References

• Aral, S., Muchnik, L., and Sundararajan, A. (2009), “Distinguishing Influence-Based Contagion From Homophily-Driven Diffusion in Dynamic Networks,” Proceedings of the National Academy of Sciences of the United States of America, 106, 21544–21549.
   PubMed Web of Science ®Google Scholar
• Doreian, P., and Stokman, F.N. (eds.) (1997), Evolution of Social Networks, Amsterdam: Routledge.
   Google Scholar
• Frank, O. (1991), “Statistical Analysis of Change in Networks,” Statistica Neerlandica, 45, 283–293.
   Google Scholar
• Frank, O., and Strauss, D. (1986), “Markov Graphs,” Journal of the American Statistical Association, 81, 832–842.
   Web of Science ®Google Scholar
• Goodreau, S., Cassels, S., Kasprzyk, D., Montao, D., Greek, A., and Morris, M. (2012), “Concurrent Partnerships, Acute Infection and HIV Epidemic Dynamics Among Young Adults in Zimbabwe,” AIDS and Behavior, 16, 312–322.
   PubMed Web of Science ®Google Scholar
• Goodreau, S.M., Carnegie, N.B., Vittinghoff, E., Lama, J.R., Sanchez, J., Grinsztejn, B., Koblin, B.A., Mayer, K.H., and Buchbinder, S.P. (2012), “What Drives the US and Peruvian HIV Epidemics in Men Who Have Sex With Men (MSM)?” PLoS ONE, 7, e50522.
   PubMed Web of Science ®Google Scholar
• Goodreau, S.M., Kitts, J.A., and Morris, M. (2009), “Birds of a Feather, or Friend of a Friend? Using Exponential Random Graph Models to Investigate Adolescent Social Networks,” Demography, 46, 103–125.
   PubMed Web of Science ®Google Scholar
• Hall, H.I., Song, R., Rhodes, P., Prejean, J., An, Q., Lee, L.M., Karon, J., Brookmeyer, R., Kaplan, E.H., McKenna, M.T., and Janssen, R.S. (2008), “Estimation of HIV Incidence in the United States,” Journal of the American Medical Association, 300, 520–529.
   PubMed Web of Science ®Google Scholar
• Handcock, M.S., Hunter, D.R., Butts, C.T., Goodreau, S.M., and Morris, M. (2008), “Statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data,” Journal of Statistical Software, 24, 1–11.
   PubMedGoogle Scholar
• Hanneke, S., Fu, W., and Xing, E.P. (2010), “Discrete Temporal Models of Social Networks,” Electronic Journal of Statistics, 4, 585–605.
   Web of Science ®Google Scholar
• Holland, P.W., and Leinhardt, S. (1977), “A Dynamic Model for Social Networks,” Journal of Mathematical Sociology, 5, 5–20.
   Web of Science ®Google Scholar
• Hummel, R.M., Hunter, D.R., and Handcock, M. S.H. (2012), “Improving Simulation-Based Algorithms for Fitting ERGMs,” Journal of Computational and Graphical Statistics, 21, 920–939.
   PubMed Web of Science ®Google Scholar
• Hunter, D.R., Goodreau, S.M., and Handcock, M.S. (2008a), “Goodness of Fit of Social Network Models,” Journal of the American Statistical Association, 103, 248–258.
   Web of Science ®Google Scholar
• Hunter, D.R., and Handcock, M.S. (2006), “Inference in Curved Exponential Family Models for Networks,” Journal of Computational and Graphical Statistics, 15, 565–583.
   Web of Science ®Google Scholar
• Hunter, D.R., Handcock, M.S., Butts, C.T., Goodreau, S.M., and Morris, M. (2008b), “Ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks,” Journal of Statistical Software, 24, 1–29.
   PubMedGoogle Scholar
• Jack, S., Moult, S., and Anderson, A.R. (2010), “An Entrepreneurial Network Evolving: Patterns of Change,” International Small Business Journal, 28, 315–337.
   Web of Science ®Google Scholar
• Krivitsky, P.N. (2012a), “Modeling of Dynamic Networks Based on Egocentric Data With Durational Information,” Technical Report 2012-01, Pennsylvania State University Department of Statistics.
   Google Scholar
• Krivitsky, P.N. (2012b), “Modeling Tie Duration in Ergm-Based Dynamic Network Models,” Technical Report 2012-02, Pennsylvania State University Department of Statistics.
   Google Scholar
• Krivitsky, P.N., and Handcock, M.S. (2014), “A Separable Model for Dynamic Networks,” Journal of the Royal Statistical Society, Series B, 76, 29–46.
   Google Scholar
• Krivitsky, P.N., Handcock, M.S., and Morris, M. (2011), “Adjusting for Network Size and Composition Effects in Exponential Family Random Graph Models,” Statistical Methodology, 8, 319–339.
   PubMedGoogle Scholar
• Mercken, L., Snijders, T.A., Steglich, C., and de Vries, H. (2009), “Dynamics of Adolescent Friendship Networks and Smoking Behavior: Social Network Analyses in Six European Countries,” Social Science and Medicine, 69, 1506–1514.
   PubMed Web of Science ®Google Scholar
• Morris, M., and Kretzschmar, M. (2000), “A Microsimulation Study of the Effect of Concurrent Partnerships on the Spread of HIV in Uganda,” Mathematical Population Studies, 8, 109–133.
   Google Scholar
• Morris, M., Kurth, A.E., Hamilton, D.T., Moody, J., and Wakefield, S. (2009), “Concurrent Partnerships and HIV Prevalence Disparities by Race: Linking Science and Public Health Practice,” American Journal of Public Health, 99, 1023–1031.
   PubMed Web of Science ®Google Scholar
• Prejean, J., Song, R., Hernandez, A., Ziebell, R., Green, T., Walker, F., Lin, L.S., An, Q., Mermin, J., Lansky, A., Hall, H.I., and (2011), “Estimated HIV Incidence in the United States, 2006--2009,” PLoS ONE, 6, e17502.
   PubMed Web of Science ®Google Scholar
• R Development Core Team (2011), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.
   Google Scholar
• Robins, G., and Morris, M. (2007), “Advances in Exponential Random Graph (p*) Models,” Social Networks, 29, 169–172.
   Web of Science ®Google Scholar
• Robins, G., and Pattison, P. (2001), “Random Graph Models for Temporal Processes in Social Networks,” Journal of Mathematical Sociology, 25, 5–41.
   Web of Science ®Google Scholar
• Robins, G., Snijders, T., Wang, P., Handcock, M., and Pattison, P. (2007), “Recent Developments in Exponential Random Graph (p*) Models for Social Networks,” Social Networks, 29, 192–215.
   Web of Science ®Google Scholar
• Runger, G., and Wasserman, S. (1980), “Longitudinal Analysis of Friendship Networks,” Social Networks, 2, 143–154.
   Web of Science ®Google Scholar
• Sasovova, Z., Mehra, A., Borgatti, S.R., and Schippers, M.C. (2010), “Network Churn: The Effects of Self-Monitoring Personality on Brokerage Dynamics,” Administrative Science Quarterly, 55, 639–670.
   Web of Science ®Google Scholar
• Snijders, T., and Koskinen, J. (2013), “Longitudinal Models,” in Exponential Random Graph Models for Social Networks, eds. D. Lusher, J. Koskinen, and G. Robins, Cambridge: Cambridge University Press, pp. 130–140.
   Google Scholar
• Snijders, T.A. (2001), “The Statistical Evaluation of Social Network Dynamics,” Sociological Methodology, 31, 361–395.
   Web of Science ®Google Scholar
• Snijders, T.A., Pattison, P.E., Robins, G.L., and Handcock, M.S. (2006), “New Specifications for Exponential Random Graph Models,” Sociological Methodology, 36, 99–153.
   Web of Science ®Google Scholar
• Snijders, T.A., Steglich, C., and Schweinberger, M. (2007), “Modeling the Co-Evolution of Networks and Behavior,” in Longitudinal Models in the Behavioral and Related Sciences, eds. K. Van Montfort, H. Oud, and A. Satorra, Mahwah, NJ: Lawrence Erlbaum, pp. 41–71.
   Google Scholar
• Strauss, D., and Ikeda, M. (1990), “Pseudolikelihood Estimation for Social Networks,” Journal of the American Statistical Association, 85, 204–212.
   Web of Science ®Google Scholar
• Wasserman, S. (1980), “Analyzing Social Networks as Stochastic Processes,” Journal of the American Statistical Association, 75, 280–294.
   Web of Science ®Google Scholar
• Wasserman, S., and Pattison, P. (1996), “Logit Models and Logistic Regressions for Social Networks. I. An Introduction to Markov Graphs and p*,” Psychometrika, 60, 401–425.
   Web of Science ®Google Scholar
• Weerman, F.M. (2011), “Delinquent Peers in Context: A Longitudinal Network Analysis of Selection and Influence Effects,” Criminology, 49, 253–286.
   Web of Science ®Google Scholar