Sampled Networks

Sampling design and inference for networks

Many real-world networks, whether social or epidemic, are not practical to observe directly. Instead, a network may be observed indirectly, from the point of view of a sample of individuals in the network. Or, only some of the relations may be observed, and noisily at that. Or, we may observe a sample of networks from a larger population.

My work develops methodology and software to permit network structure to be recovered from such indirectly observed relational data.

Pavel N. Krivitsky, Pietro Coletti, and Niel Hens, A tale of two datasets: Representativeness and generalisability of inference for samples of networks, Journal of the American Statistical Association, pp. 1–21, 2023. doi:10.1080/01621459.2023.2242627

Pavel N. Krivitsky, Pietro Coletti, and Niel Hens, Rejoinder to discussion of “A tale of two datasets: Representativeness and generalisability of inference for samples of networks”, Journal of the American Statistical Association, vol. 118, no. 544, pp. 2235–2238, 2023. doi:10.1080/01621459.2023.2280383

Pavel N. Krivitsky, Martina Morris, and Michał Bojanowski, Impact of survey design on estimation of exponential-family random graph models from egocentrically-sampled data, Social Networks, 2021. doi:10.1016/j.socnet.2020.10.001

Michael Schweinberger, Pavel N. Krivitsky, Carter T. Butts, and Jonathan R. Stewart, Exponential-family models of random graphs: Inference in finite, super and infinite population scenarios, Statistical Science, vol. 35, no. 4, pp. 627–662, 2020. doi:10.1214/19-STS743

Pavel N. Krivitsky and Martina Morris, Inference for social network models from egocentrically-sampled data, with application to understanding persistent racial disparities in HIV prevalence in the US, Annals of Applied Statistics, vol. 11, no. 1, pp. 427–455, 2017. doi:10.1214/16-AOAS1010

Vishesh Karwa, Pavel N. Krivitsky, and Aleksandra B. Slavković, Sharing social network data: Differentially private estimation of exponential-family random graph models, Journal of the Royal Statistical Society, Series C, vol. 66, no. 3, pp. 481–500, 2017. doi:10.1111/rssc.12185

Pavel N. Krivitsky and Eric D. Kolaczyk, On the question of effective sample size in network modeling: An asymptotic inquiry, Statistical Science, vol. 30, no. 2, pp. 184–198, 2015. doi:10.1214/14-STS502

Vishesh Karwa, Aleksandra B Slavković, and Pavel Krivitsky, Differentially private exponential random graphs, In Proc. Privacy in statistical databases, 2014, vol. 8744, pp. 143–155. doi:10.1007/978-3-319-11257-2_12

Pavel N. Krivitsky, Modeling of dynamic networks based on egocentric data with durational information, Pennsylvania State University Department of Statistics, no. 2012-01, 2012. arXiv:2203.06866

Pavel N. Krivitsky, Mark S. Handcock, and Martina Morris, Adjusting for network size and composition effects in exponential-family random graph models, Statistical Methodology, vol. 8, no. 4, pp. 319–339, 2011. doi:10.1016/j.stamet.2011.01.005

ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks

ergm.multi: Fit, Simulate and Diagnose Exponential-Family Models for Multiple or Multilayer Networks

ergm.ego: Fit, Simulate and Diagnose Exponential-Family Random Graph Models to Egocentrically Sampled Network Data

egor: Import and Analyse Ego-Centered Network Data