Papers

Saman Forouzandeh, Rohitash Chandra, and Pavel N. Krivitsky, Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systems, Under review, 2024.

Victoria L. Leaver, Robert G. Clark, Pavel N. Krivitsky, and Carole L. Birrell, A comparison of likelihood-based methods for size-biased sampling, Journal of Statistical Planning and Inference, vol. 230, p. 106115, 2024. doi:10.1016/j.jspi.2023.106115

David Dekker, David Krackhardt, Patrick Doreian, and Pavel N. Krivitsky, Balance correlations, agentic zeros, and networks: The structure of 192 years of war and peace, PLOS ONE, vol. 19, no. 12, pp. 1–32, 2024. doi:10.1371/journal.pone.0315088

Pavel N. Krivitsky, Alina R. Kuvelkar, and David R. Hunter, Likelihood-based inference for exponential-family random graph models via linear programming, Electronic Journal of Statistics, vol. 17, no. 2, 2023. doi:10.1214/23-EJS2176

Pavel N. Krivitsky, David R. Hunter, Martina Morris, and Chad Klumb, ergm 4: New features for analyzing exponential-family random graph models, Journal of Statistical Software, vol. 105, no. 6, pp. 1–44, 2023. doi:10.18637/jss.v105.i06

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

Rohitash Chandra, Mahir Jain, Manavendra Maharana, and Pavel N. Krivitsky, Revisiting bayesian autoencoders with MCMC, IEEE Access, vol. 10, pp. 40482–40495, 2022. doi:10.1109/access.2022.3163270

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

Rohitash Chandra, Ayush Bhagat, Manavendra Maharana, and Pavel N. Krivitsky, Bayesian graph convolutional neural networks via tempered MCMC, IEEE Access, vol. 9, pp. 130353–130365, 2021. doi:10.1109/access.2021.3111898

Pavel N. Krivitsky, Laura M. Koehly, and Christopher Steven Marcum, Exponential-family random graph models for multi-layer networks, Psychometrika, vol. 85, no. 3, pp. 630–659, 2020. doi:10.1007/s11336-020-09720-7

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

Pavel N. Krivitsky, Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models, Computational Statistics & Data Analysis, vol. 107, pp. 149–161, 2017. doi:10.1016/j.csda.2016.10.015

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 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

Noel Cressie, Sandy Burden, Walter Davis, Pavel N. Krivitsky, Payam Mokhtarian, Thomas Suesse, and Andrew Zammit-Mangion, Capturing multivariate spatial dependence: Model, estimate, and then predict (Discussion Paper), Statistical Science, vol. 30, no. 2, pp. 170–175, 2015. doi:10.1214/15-STS517

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

Luke Mazur, Thomas Suesse, and Pavel N. Krivitsky, Investigating foreign portfolio investment holdings: Gravity model with social network analysis, International Journal of Finance & Economics, vol. 27, no. 1, pp. 554–570, 2020. doi:10.1002/ijfe.2168

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, Exponential-family random graph models for valued networks, Electronic Journal of Statistics, vol. 6, pp. 1100–1128, 2012. doi:10.1214/12-EJS696

David R. Hunter, Pavel N. Krivitsky, and Michael Schweinberger, Computational statistical methods for social network models, Journal of Computational and Graphical Statistics, vol. 21, no. 4, pp. 856–882, 2012. doi:10.1080/10618600.2012.732921

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

Pavel N. Krivitsky, Mark S. Handcock, Adrian E. Raftery, and Peter D. Hoff, Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models, Social Networks, vol. 31, no. 3, pp. 204–213, 2009. doi:10.1016/j.socnet.2009.04.001

Pavel N. Krivitsky and Mark S. Handcock, Fitting position latent cluster models for social networks with, Journal of Statistical Software, vol. 24, no. 5, pp. 1–23, 2008. doi:10.18637/jss.v024.i05

Marijka Batterham and Pavel N. Krivitsky, Relationship between statistics anxiety and final marks in an introductory biostatistics in undergraduate health sciences students, In Proc. OZCOTS 2021: Proceedings of the 10th australian conference on teaching statistics, 2021 [URL]

Yue Ma, Yan-Xia Lin, Pavel N. Krivitsky, and Bradley Wakefield, Quantifying protection level of a noise candidate for noise multiplication masking scheme, In Proc. Privacy in statistical databases, 2018, vol. 11126, pp. 279–293. doi:10.1007/978-3-319-99771-1_19

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, Pedro M. A. Ferreira, and Rahul Telang, Network neighbor effects on customer churn in cell phone networks, In Proc. Proceedings of the 7th symposium on statistical challenges in e-commerce research (SCECR 2011), 2011 [URL]

Adrian E. Raftery, Michael A. Newton, Jaya M. Satagopan, and Pavel N. Krivitsky, Estimating the integrated likelihood via posterior simulation using the harmonic mean identity, In Proc. Bayesian statistics 8: Proceedings of the valencia/ISBA 8th world meeting on bayesian statistics, 2007, vol. 8, pp. 317–416. doi:10.1093/oso/9780199214655.003.0015

Pavel N. Krivitsky, David R. Hunter, Martina Morris, and Chad Klumb, ergm 4: Computational improvements, 2022. arXiv:2203.08198

Pavel N. Krivitsky, Michał Bojanowski, and Martina Morris, Inference for exponential-family random graph models from egocentrically-sampled data with alter–alter relations, 2019.  [URL]

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