Download these references in BibTeX.
Articles
Under Review

Pavel N. Krivitsky, David R. Hunter, Martina Morris, and Chad Klumb (2021).
ergm
4.0: New features and improvements. 
Marijka Batterham and Pavel N. Krivitsky (2021). Relationship Between Statistics Anxiety and Final Marks in an Introductory Biostatistics in Undergraduate Health Sciences Students.

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

Rohitash Chandra, Mahir Jain, Manavendra Maharana, Pavel N. Krivitsky (2021). Revisiting Bayesian Autoencoders with MCMC.

Thomas Suesse, Pavel N. Krivitsky, and Olivier Thas (2019). Fitting Marginalized Exponential Random Graph Models via GEE.
PeerReviewed

Pavel N. Krivitsky, Michał Bojanowski, and Martina Morris (2020). Impact of Egocentric Survey Design on Estimable Network Features. Social Networks.

Pavel N. Krivitsky, Laura Koehly, and Christopher S. Marcum (2020). ExponentialFamily Random Graph Models for MultiLayer Networks. Psychometrika, 85(3):630–659.

Luke Mazur, Thomas Suesse, and Pavel N. Krivitsky (2020). Investigating Foreign Portfolio Investment Holdings: Gravity Model with Social Network Analysis. International Journal of Finance and Economics, 1–17.

Michael Schweinberger, Pavel N. Krivitsky, Carter T. Butts, and Jonathan Stewart (2020). ExponentialFamily Models of Random Graphs: Inference in Finite, Super, and Infinite Population Scenarios. Statistical Science, 35(4):627–662.

Pavel N. Krivitsky and Carter T. Butts (2017). ExponentialFamily Random Graph Models for RankOrder Relational Data. Sociological Methodology, 47(1):68–112.

Pavel N. Krivitsky and Martina Morris (2017). Inference for Social Network Models from EgocentricallySampled Data, with Application to Understanding Persistent Racial Disparities in HIV Prevalence in the US. Annals of Applied Statistics, 11(1):427–455.

Pavel N. Krivitsky (2017). Using Contrastive Divergence to Seed Monte Carlo MLE for ExponentialFamily Random Graph Models. Computational Statistics and Data Analysis, 107:149–161.

Vishesh Karwa, Pavel N. Krivitsky, and Aleksandra B. Slavkovi'c (2016). Sharing Social Network Data: Differentially Private Estimation of ExponentialFamily Random Graph Models. Journal of the Royal Statistical Society, Series C, 66(3):481–500.

Noel Cressie, Sandy Burden, Walter Davis, Pavel N. Krivitsky, Payam Mokhtarian, Thomas Suesse, and Andrew ZammitMangion. Capturing Multivariate Spatial Dependence: Model, Estimate, and then Predict (Discussion Paper). Statistical Science, 30(2):170–175.

Pavel N. Krivitsky and Eric D. Kolaczyk (2015). On the Question of Effective Sample Size in Network Modeling: An Asymptotic Inquiry. Statistical Science, 30(2):184–198.

Nicole Bohme Carnegie, Pavel N. Krivitsky, David R. Hunter, and Steven M. Goodreau (2015). An Approximation Method for Improving Dynamic Network Model Fitting. Journal of Computational and Graphical Statistics, 24(2):502–519.

Pavel N. Krivitsky and Mark S. Handcock (2014). A Separable Model for Dynamic Networks. Journal of the Royal Statistical Society, Series B, (76)1:29–46.

David R. Hunter, Pavel N. Krivitsky, and Michael Schweinberger (2012). Computational Statistical Methods for Social Network Models (Invited Paper) Journal of Computational and Graphical Statistics, 21(4):856–882.

Pavel N. Krivitsky (2012). ExponentialFamily Random Graph Models for Valued Networks. Electronic Journal of Statistics, 6:1100–1128.

Pavel N. Krivitsky, Mark S. Handcock, and Martina Morris (2011). Adjusting for Network Size and Composition Effects in ExponentialFamily Random Graph Models. Statistical Methodology, 8(4):319–339.

Pavel N. Krivitsky, Mark S. Handcock, Adrian E. Raftery, and Peter D. Hoff (2009). Representing Degree Distributions, Clustering, and Homophily in Social Networks with Latent Cluster Random Effects Models. Social Networks, 31(3):204213.

Pavel N. Krivitsky and Mark S. Handcock (2008). Fitting Latent Cluster Models for Networks with
latentnet
. Journal of Statistical Software, 24(5):1–23.
In Proceedings

Yue Ma, YanXia Lin, Pavel N. Krivitsky, and Bradley Wakefield (2018). Quantifying Protection Level of a Noise Candidate for Noise Multiplication Masking Scheme. Privacy in Statistical Databases in Lecture Notes in Computer Science, 11126:279–293.

YanXia Lin and Pavel N. Krivitsky (2018). Reviewing the Methods of Estimating the Density Function Based on Masked Data. Privacy in Statistical Databases in Lecture Notes in Computer Science, 11126:231–246.

Vishesh Karwa, Aleksandra B. Slavković, and Pavel Krivitsky (2014). J. DomingoFerrer (ed.) Differentially Private Exponential Random Graphs. Privacy in Statistical Databases in Lecture Notes in Computer Science, 8744:143–155.

Pavel N. Krivitsky, Pedro M. A. Ferreira, and Rahul Telang (2011). Network Neighbor Effects on Customer Churn in Cell Phone Betworks. Proceedings of the 7th Symposium on Statistical Challenges in ECommerce Research (SCECR 2011).

Adrian E. Raftery, Michael A. Newton, Jaya M. Satagopan, and Pavel N. Krivitsky (2006). J. M. Bernardo, M.J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West (eds.) Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity. Bayesian Statistics 8: Proceedings of the Eighth Valencia International Meeting, 8:371–416.
Technical Reports

Pavel N. Krivitsky (April 2012). Modeling of Dynamic Networks based on Egocentric Data with Durational Information. Pennsylvania State University Department of Statistics Technical Report, TR1201.

Pavel N. Krivitsky (April 2012). Modeling Tie Duration in ERGMBased Dynamic Network Models. Pennsylvania State University Department of Statistics Technical Report, TR1202.

Michael Schweinberger, Pavel N. Krivitsky, and Carter T. Butts (2017). A note on the role of projectivity in likelihoodbased inference for random graph models.
Theses
The material in it has been corrected and published as articles and technical reports listed above, but if you would like the original, contact me.
Ph.D. Thesis (2009)
Statistical Models for Social Network Data and Processes 1. Extensions to latent cluster models for social networks 2. ERGMderived processes under changing network size 3. ERGMbased models and inference for dynamic networks
 Abstract
 This work deals with three areas of network modeling. First, in the area of latent space modeling of social networks, it develops and extends latent cluster social network models by adding random effects and providing efficient algorithms for fitting these models. Second, it explores properties of ERGM and ERGMbased models under changing network size, and proposes a way of addressing the problems that arise. Third, in the area of dynamic networks, it proposes and develops a model separating tie formation process from tie dissolution process, facilitating flexible and realistic simulation of dynamic networks. Methods for integrating of adjustments for network size changes into the dynamic models are also developed.
 Committee
 Mark S. Handcock (advisor, reader), Martina Morris (reader), Peter D. Hoff (reader), Steven M. Goodreau, Adrian E. Raftery, and Ira M. Longini, Pedro Domingos (GSR)
Undergraduate Senior Honors Thesis (2003)
The Effect of Integration Cell Size and *In Situ* Target Strength Calculation Method on Acoustic Fish Density Estimates for Alewife Lakes of New York State
 Advisors
 Steven J. Schwager, Lars G. Rudstam