A dynamic process interpretation of the sparse ERGM reference model

References

• Bernard, H. R., Johnsen, E. C., Killworth, P. D., & Robinson, S. (1989). Estimating the size of an average personal network and of an event subpopulation. In M. Kochen (Ed.), The small world (pp. pages 159–175). Norwood, NJ: Ablex.
   Google Scholar
• Butts, C. T. (2008). Social network analysis with sna. Journal of Statistical Software, 24, 6. doi:10.18637/jss.v024.i06
   Google Scholar
• Butts, C. T. (2009). Revisiting the foundations of network analysis. Science, 325, 414–416. doi:10.1126/science.1171022
   PubMed Web of Science ®Google Scholar
• Butts, C. T. (2011). Bernoulli graph bounds for general random graphs. Sociological Methodology, 41, 299–345. doi:10.1111/j.1467-9531.2011.01246.x
   Web of Science ®Google Scholar
• Butts, C. T., Acton, R. M., Hipp, J. R., & Nagle, N. N. (2012). Geographical variability and network structure. Social Networks, 34, 82–100. doi:10.1016/j.socnet.2011.08.003
   Web of Science ®Google Scholar
• Butts, C. T., & Almquist, Z. W. (2015). A flexible parameterization for baseline mean degree in multiple-network ERGMs. Journal of Mathematical Sociology, 39(3), 163–167. doi:10.1080/0022250X.2014.967851
   PubMed Web of Science ®Google Scholar
• Caselli, G., Vallin, J., & Wunsch, G. (2006). Demography: Analysis and synethesis. Amsterdam, Netherlands: Elsevier.
   Google Scholar
• Feld, S. (1981). The focused organization of social ties. American Journal of Sociology, 1986, 1015–1035. doi:10.1086/227352
   Google Scholar
• Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). statnet: Software tools for the representation, visualization, analysis and simulation of network data. Journal of Statistical Software, 24(1), 1–11. doi:10.18637/jss.v024.i01
   PubMedGoogle Scholar
• Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). ergm: A package to fit, simulate and diagnose exponential-family models for networks. Journal of Statistical Software, 24, 3. doi:10.18637/jss.v024.i03
   Google Scholar
• Koskinen, J., Caimo, A., & Lomi, A. (2015). Simultaneous modeling of initial conditions and time heterogeneity in dynamic networks: An application to foreign direct investments. Network Science, 3(1), 58–77. doi:10.1017/nws.2015.3
   Web of Science ®Google Scholar
• Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128. doi:10.1214/12-EJS696
   PubMed Web of Science ®Google Scholar
• Krivitsky, P. N., & Butts, C. T. (2017). Exponential-family random graph models for rank-order relational data. Sociological Methodology, 47, 68–112. doi:10.1177/0081175017692623
   Web of Science ®Google Scholar
• Krivitsky, P. N., & Handcock, M. S. (2014). A separable model for dynamic networks. Journal of the Rotal Statistical Society, Series B, 76(1), 29–46. doi:10.1111/rssb.12014
   PubMed Web of Science ®Google Scholar
• Krivitsky, P. N., Handcock, M. S., & Morris, M. (2011). Adjusting for network size and composition effects in exponential-family random graph models. Statistical Methodology, 8(4), 319–339. doi:10.1016/j.stamet.2011.01.005
   PubMedGoogle Scholar
• Krivitsky, P. N., & Kolaczyk, E. D. (2015). On the question of effective sample size in network modeling: An asymptotic inquiry. Statistical Science, 30, 184–198. doi:10.1214/14-STS502
   PubMed Web of Science ®Google Scholar
• Lerner, J., Indlekofer, N., Nick, B., & Brandes, U. (2013). Conditional independence in dynamic networks. Journal of Mathematical Psychology, 57(6), 275–283. Social Networks. doi:10.1016/j.jmp.2012.03.002
   Web of Science ®Google Scholar
• Leskovec, J., Kleinberg, J., & Faloutsos, C. (2007). Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data, 1, 1. doi:10.1145/1217299.1217301
   Google Scholar
• Mayhew, B. H. (1984). Baseline models of sociological phenomena. Journal of Mathematical Sociology, 9, 259–281. doi:10.1080/0022250X.1984.9989948
   Web of Science ®Google Scholar
• McCormick, T. H., Salganik, M. J., & Zheng, T. (2008). How many people do you know?: Efficiently estimating personal network size. Journal of the American Statistical Association, 105(489), 59–70. doi:10.1198/jasa.2009.ap08518
   Web of Science ®Google Scholar
• Nowicki, K., & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96, 1077–1087. doi:10.1198/016214501753208735
   Web of Science ®Google Scholar
• Pattison, P. E., & Robins, G. L. (2002). Neighborhood-based models for social networks. Sociological Methodology, 32, 301–337. doi:10.1111/1467-9531.00119
   Web of Science ®Google Scholar
• R Core Team. (2018). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
   Google Scholar
• Schweinberger, M., Krivitsky, P. N., & Butts, C. T. (2017). Foundations of finite-, super-, and infinite-population random graph inference (arXiv:1707.04800).
   Google Scholar
• Snijders, T. A. B. (1996). Stochastic actor-oriented models for network change. Journal of Mathematical Sociology, 23, 149–172. doi:10.1080/0022250X.1996.9990178
   Google Scholar
• Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31, 361–395. doi:10.1111/0081-1750.00099
   Web of Science ®Google Scholar
• Snijders, T. A. B., & Nowicki, K. (1997). Estimation and prediction for stochastic block models for graphs with latent block structure. Journal of Classification, 14, 75–100. doi:10.1007/s003579900004
   Web of Science ®Google Scholar
• Steglich, C., Snijders, T. A. B., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40(1), 329–393. doi:10.1111/j.1467-9531.2010.01225.x
   Web of Science ®Google Scholar
• Wang, Y. J., & Wong, G. Y. (1987). Stochastic blockmodels for directed graphs. Journal of the American Statistical Association, 82, 8–19. doi:10.1080/01621459.1987.10478385
   Web of Science ®Google Scholar