Dynamic Networks

Modelling and prediction of networks that change over time

Models for networks that evolve over time have manifold application in areas as diverse as epidemiology, social sciences, and marketing. A major component of my research is the development of realistic yet parsimonious models for the evolution of social networks, and fitting them based on available data. These models have been applied to the modelling and simulation of disease spread.

Pavel N. Krivitsky and Carter T. Butts, Exponential-family random graph models for rank-order relational data, Sociological Methodology, vol. 47, no. 1, pp. 68–112, 2017. doi:10.1177/0081175017692623

Nicole Bohme Carnegie, Pavel N. Krivitsky, David R. Hunter, and Steven M. Goodreau, An approximation method for improving dynamic network model fitting, Journal of Computational and Graphical Statistics, vol. 24, no. 2, pp. 502–519, 2015. doi:10.1080/10618600.2014.903087

Pavel N. Krivitsky and Mark S. Handcock, A separable model for dynamic networks, Journal of the Royal Statistical Society, Series B, vol. 76, no. 1, pp. 29–46, 2014. doi:10.1111/rssb.12014

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, Modeling tie duration in ERGM-based dynamic network models, Pennsylvania State University Department of Statistics, no. 2012-02, 2012. arXiv:2203.11817

tergm: Fit, Simulate and Diagnose Models for Network Evolution Based on Exponential-Family Random Graph Models