By Dr Pavel Krivitsky

Book Chapters

Raftery AE; Newton MA; Satagopan JM; Krivitsky PN, 2007, 'Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity', in Bayesian Statistics 8, Oxford University PressOxford, pp. 381 - 426, http://dx.doi.org/10.1093/oso/9780199214655.003.0015

Journal articles

Dekker D; Krackhardt D; Doreian P; Krivitsky PN, 2024, 'Balance correlations, agentic zeros, and networks: The structure of 192 years of war and peace', PLoS ONE, 19, http://dx.doi.org/10.1371/journal.pone.0315088

Leaver VL; Clark RG; Krivitsky PN; Birrell CL, 2024, 'A comparison of likelihood-based methods for size-biased sampling', Journal of Statistical Planning and Inference, 230, http://dx.doi.org/10.1016/j.jspi.2023.106115

Krivitsky PN; Coletti P; Hens N, 2024, 'Correction', Journal of the American Statistical Association, 119, pp. 1694 - 1695, http://dx.doi.org/10.1080/01621459.2024.2344624

Krivitsky PN; Coletti P; Hens N, 2023, 'A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks', Journal of the American Statistical Association, 118, pp. 2213 - 2224, http://dx.doi.org/10.1080/01621459.2023.2242627

Krivitsky PN; Hunter DR; Morris M; Klumb C, 2023, 'ergm 4: New Features for Analyzing Exponential-Family Random Graph Models', Journal of Statistical Software, 105, pp. 1 - 44, http://dx.doi.org/10.18637/jss.v105.i06

Krivitsky PN; Kuvelkar AR; Hunter DR, 2023, 'Likelihood-based inference for exponential-family random graph models via linear programming', Electronic Journal of Statistics, 17, pp. 3337 - 3356, http://dx.doi.org/10.1214/23-EJS2176

Krivitsky PN; Coletti P; Hens N, 2023, 'Rejoinder to Discussion of “A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks”', Journal of the American Statistical Association, 118, pp. 2235 - 2238, http://dx.doi.org/10.1080/01621459.2023.2280383

Krivitsky PN; Morris M; Bojanowski M, 2022, 'Impact of survey design on estimation of exponential-family random graph models from egocentrically-sampled data', Social Networks, 69, pp. 22 - 34, http://dx.doi.org/10.1016/j.socnet.2020.10.001

Mazur L; Suesse T; Krivitsky PN, 2022, 'Investigating foreign portfolio investment holdings: Gravity model with social network analysis', International Journal of Finance and Economics, 27, pp. 554 - 570, http://dx.doi.org/10.1002/ijfe.2168

Chandra R; Jain M; Maharana M; Krivitsky PN, 2022, 'Revisiting Bayesian Autoencoders With MCMC', IEEE Access, 10, pp. 40482 - 40495, http://dx.doi.org/10.1109/ACCESS.2022.3163270

Chandra R; Bhagat A; Maharana M; Krivitsky PN, 2021, 'Bayesian Graph Convolutional Neural Networks via Tempered MCMC', IEEE Access, 9, pp. 130353 - 130365, http://dx.doi.org/10.1109/ACCESS.2021.3111898

Krivitsky PN; Koehly LM; Marcum CS, 2020, 'Exponential-Family Random Graph Models for Multi-Layer Networks', Psychometrika, 85, pp. 630 - 659, http://dx.doi.org/10.1007/s11336-020-09720-7

Schweinberger M; Krivitsky PN; Butts CT; Stewart JR, 2020, 'Exponential-Family Models of Random Graphs: Inference in Finite, Super and Infinite Population Scenarios', Statistical Science, 35, pp. 627 - 662, http://dx.doi.org/10.1214/19-STS743

Krivitsky PN; Butts CT, 2017, 'Exponential-family random graph models for rank-order relational data', Sociological Methodology, 47, pp. 68 - 112, http://dx.doi.org/10.1177/0081175017692623

Karwa V; Krivitsky PN; Slavković AB, 2017, 'Sharing social network data: differentially private estimation of exponential family random-graph models', Journal of the Royal Statistical Society. Series C: Applied Statistics, 66, pp. 481 - 500, http://dx.doi.org/10.1111/rssc.12185

Krivitsky PN; Morris M, 2017, '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, 11, pp. 427 - 455, http://dx.doi.org/10.1214/16-AOAS1010

Krivitsky PN, 2017, 'Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models', Computational Statistics and Data Analysis, 107, pp. 149 - 161, http://dx.doi.org/10.1016/j.csda.2016.10.015

Carnegie NB; Krivitsky PN; Hunter DR; Goodreau SM, 2015, 'An Approximation Method for Improving Dynamic Network Model Fitting', Journal of Computational and Graphical Statistics, 24, pp. 502 - 519, http://dx.doi.org/10.1080/10618600.2014.903087

Cressie N; Burden S; Davis W; Krivitsky PN; Mokhtarian P; Suesse T; Zammit-Mangion A, 2015, 'Capturing multivariate spatial dependence: Model, estimate and then predict', Statistical Science, 30, pp. 170 - 175, http://dx.doi.org/10.1214/15-STS517

Krivitsky PN; Kolaczyk ED, 2015, 'On the question of effective sample size in network modeling: An asymptotic inquiry', Statistical Science, 30, pp. 184 - 198, http://dx.doi.org/10.1214/14-STS502

Krivitsky PN; Handcock MS, 2014, 'A separable model for dynamic networks', Journal of the Royal Statistical Society. Series B: Statistical Methodology, 76, pp. 29 - 46, http://dx.doi.org/10.1111/rssb.12014

Krivitsky PN, 2012, 'Exponential-family random graph models for valued networks', Electronic Journal of Statistics, 6, pp. 1100 - 1128, http://dx.doi.org/10.1214/12-EJS696

Hunter DR; Krivitsky PN; Schweinberger M, 2012, 'Computational statistical methods for social network models', Journal of Computational and Graphical Statistics, 21, pp. 856 - 882, http://dx.doi.org/10.1080/10618600.2012.732921

Krivitsky PN; Handcock MS; Morris M, 2011, 'Adjusting for network size and composition effects in exponential-family random graph models', Statistical Methodology, 8, pp. 319 - 339, http://dx.doi.org/10.1016/j.stamet.2011.01.005

Krivitsky PN; Handcock MS; Raftery AE; Hoff PD, 2009, 'Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models', Social Networks, 31, pp. 204 - 213, http://dx.doi.org/10.1016/j.socnet.2009.04.001

Krivitsky PN; Handcock MS, 2008, 'Fitting position latent cluster models for social networks with latentnet', Journal of Statistical Software, 24, http://dx.doi.org/10.18637/jss.v024.i05

Conference Papers

Ma Y; Lin YX; Krivitsky PN; Wakefield B, 2018, 'Quantifying the protection level of a noise candidate for noise multiplication masking scheme', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature, pp. 279 - 293, http://dx.doi.org/10.1007/978-3-319-99771-1_19

Lin YX; Krivitsky PN, 2018, 'Reviewing the methods of estimating the density function based on masked data', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature, pp. 231 - 246, http://dx.doi.org/10.1007/978-3-319-99771-1_16

Karwa V; Slavković AB; Krivitsky P, 2014, 'Differentially private exponential random graphs', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 143 - 155, http://dx.doi.org/10.1007/978-3-319-11257-2_12

Software / Code

Krivitsky PN, 2022, ergm.multi: Fit, Simulate and Diagnose Exponential-Family Models for Multiple or Multilayer Networks, Comprehensive R Archive Network (CRAN), R package, Published: 2022, Software / Code, https://cran.r-project.org/package=ergm.multi

Krenz T; Krivitsky PN; Vacca R; Bojanowski M, 2019, egor: Import and Analyse Ego-Centered Network Data, CRAN, Editor(s): Krenz T, R package, Published: 2019, Software / Code, https://cran.r-project.org/package=egor

Handcock MS; Hunter DR; Butts CT; Goodreau SM; Krivitsky PN; Morris M, 2019, ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks, CRAN, Editor(s): Krivitsky PN, R package, Published: 2019, Software / Code, https://cran.r-project.org/package=ergm

Krivitsky PN, 2019, ergm.count: Fit, Simulate and Diagnose Exponential-Family Models for Networks with Count Edges, CRAN, Editor(s): Krivitsky PN, R package, Published: 2019, Software / Code, https://cran.r-project.org/package=ergm.count

Krivitsky PN, 2019, ergm.ego: Fit, Simulate and Diagnose Exponential-Family Random Graph Models to Egocentrically Sampled Network Data, CRAN, Editor(s): Krivitsky PN, R package, Published: 2019, Software / Code, https://cran.r-project.org/package=ergm.ego

Krivitsky PN, 2019, ergm.rank: Fit, Simulate and Diagnose Exponential-Family Models for Rank-Order Relational Data, CRAN, Editor(s): Krivitsky PN, R package, Published: 2019, Software / Code, https://cran.r-project.org/package=ergm.rank

Butts CT; Leslie-Cook A; Krivitsky PN; Bender-deMoll S, 2019, networkDynamic: Dynamic Extensions for Network Objects, CRAN, Editor(s): Bender-deMoll S, R package, Published: 2019, Software / Code, https://cran.r-project.org/package=networkDynamic

Krivitsky PN, 2019, statnet.common: Common R Scripts and Utilities Used by the Statnet Project Software, CRAN, Editor(s): Krivitsky PN, R package, Published: 2019, Software / Code, https://cran.r-project.org/package=statnet.common

Krivitsky PN; Handcock MS, 2019, tergm: Fit, Simulate and Diagnose Models for Network Evolution Based on Exponential-Family Random Graph Models, CRAN, Editor(s): Krivitsky PN, R package, Published: 2019, Software / Code, https://cran.r-project.org/package=tergm

Krivitsky PN; Handcock MS, 2018, latentnet: Latent Position and Cluster Models for Statistical Networks, CRAN, Editor(s): Krivitsky PN, R package, Published: 2018, Software / Code, https://cran.r-project.org/package=latentnet

Preprints

Forouzandeh S; Krivitsky PN; Chandra R, 2025, Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systems, http://arxiv.org/abs/2502.19271v1

Krivitsky PN; Coletti P; Hens N, 2023, Rejoinder to Discussion of "A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks'', http://dx.doi.org/10.48550/arxiv.2312.06028

Dekker D; Krackhardt D; Doreian P; Krivitsky PN, 2023, Balance Correlations, Agentic Zeros, and Networks: The Structure of 192 Years of War and Peace, http://dx.doi.org/10.48550/arxiv.2312.04358

Krivitsky PN; Hunter DR; Morris M; Klumb C, 2022, ergm 4: Computational Improvements, http://dx.doi.org/10.48550/arxiv.2203.08198

Krivitsky PN, 2022, Modeling of Dynamic Networks based on Egocentric Data with Durational Information, http://dx.doi.org/10.48550/arxiv.2203.06866

Krivitsky PN, 2022, Modeling Tie Duration in ERGM-Based Dynamic Network Models, http://dx.doi.org/10.48550/arxiv.2203.11817

Krivitsky PN; Coletti P; Hens N, 2022, A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks, http://dx.doi.org/10.48550/arxiv.2202.03685

Krivitsky PN; Kuvelkar AR; Hunter DR, 2022, Likelihood-based Inference for Exponential-Family Random Graph Models via Linear Programming, http://dx.doi.org/10.48550/arxiv.2202.03572

Krivitsky PN; Hunter DR; Morris M; Klumb C, 2021, ergm 4: New features, http://dx.doi.org/10.48550/arxiv.2106.04997

Chandra R; Bhagat A; Maharana M; Krivitsky PN, 2021, Bayesian graph convolutional neural networks via tempered MCMC, http://dx.doi.org/10.48550/arxiv.2104.08438

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