Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models. - Pavel N. Krivitsky; Mark S. Handcock; Adrian E. Raftery; Peter D. Hoff (Soc. Networks, 2009)

Differentially Private Exponential Random Graphs. - Vishesh Karwa; Aleksandra B. Slavkovic; Pavel N. Krivitsky (Privacy in Statistical Databases, 2014)

Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models. - Vishesh Karwa; Pavel N. Krivitsky; Aleksandra B. Slavkovic (CoRR, 2015)

Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models. - Pavel N. Krivitsky (Comput. Stat. Data Anal., 2017)

Reviewing the Methods of Estimating the Density Function Based on Masked Data. - Yan-Xia Lin; Pavel N. Krivitsky (PSD, 2018)

Quantifying the Protection Level of a Noise Candidate for Noise Multiplication Masking Scheme. - Yue Ma; Yan-Xia Lin; Pavel N. Krivitsky; Bradley Wakefield (PSD, 2018)

Bayesian Graph Convolutional Neural Networks via Tempered MCMC. - Rohitash Chandra; Ayush Bhagat; Manavendra Maharana; Pavel N. Krivitsky (IEEE Access, 2021)

Revisiting Bayesian Autoencoders With MCMC. - Rohitash Chandra; Mahir Jain; Manavendra Maharana; Pavel N. Krivitsky (IEEE Access, 2022)

Modeling Tie Duration in ERGM-Based Dynamic Network Models. - Pavel N. Krivitsky (CoRR, 2022)

Impact of survey design on estimation of exponential-family random graph models from egocentrically-sampled data. - Pavel N. Krivitsky; Martina Morris; Michal Bojanowski (Soc. Networks, 2022)

ergm 4: New Features for Analyzing Exponential-Family Random Graph Models. - Pavel N. Krivitsky; David R. Hunter; Martina Morris; Chad Klumb (J. Stat. Softw., 2023)

Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systems. - Saman Forouzandeh; Pavel N. Krivitsky; Rohitash Chandra (Expert Syst. Appl., 2025)