Select Publications
Book Chapters
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
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
,2024, 'Correction', Journal of the American Statistical Association, 119, pp. 1694 - 1695, http://dx.doi.org/10.1080/01621459.2024.2344624
,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
,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
,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
,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
,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
,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
,2022, 'Revisiting Bayesian Autoencoders With MCMC', IEEE Access, 10, pp. 40482 - 40495, http://dx.doi.org/10.1109/ACCESS.2022.3163270
,2021, 'Bayesian Graph Convolutional Neural Networks via Tempered MCMC', IEEE Access, 9, pp. 130353 - 130365, http://dx.doi.org/10.1109/ACCESS.2021.3111898
,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
,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
,2017, 'Exponential-family random graph models for rank-order relational data', Sociological Methodology, 47, pp. 68 - 112, http://dx.doi.org/10.1177/0081175017692623
,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
,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
,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
,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
,2015, 'Capturing multivariate spatial dependence: Model, estimate and then predict', Statistical Science, 30, pp. 170 - 175, http://dx.doi.org/10.1214/15-STS517
,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
,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
,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
,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
,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
,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
,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
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
,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
,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
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
,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
,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
,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
,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
,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
,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
,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
,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
,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
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
,2022, ergm 4: Computational Improvements, http://dx.doi.org/10.48550/arxiv.2203.08198
,2022, Modeling of Dynamic Networks based on Egocentric Data with Durational Information, http://dx.doi.org/10.48550/arxiv.2203.06866
,2022, Modeling Tie Duration in ERGM-Based Dynamic Network Models, http://dx.doi.org/10.48550/arxiv.2203.11817
,2022, A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks, http://dx.doi.org/10.48550/arxiv.2202.03685
,2022, Likelihood-based Inference for Exponential-Family Random Graph Models via Linear Programming, http://dx.doi.org/10.48550/arxiv.2202.03572
,2021, ergm 4: New features, http://dx.doi.org/10.48550/arxiv.2106.04997
,2021, Bayesian graph convolutional neural networks via tempered MCMC, http://dx.doi.org/10.48550/arxiv.2104.08438
,2021, Revisiting Bayesian Autoencoders with MCMC, http://dx.doi.org/10.48550/arxiv.2104.05915
,2019, Exponential-Family Random Graph Models for Multi-Layer Networks, http://dx.doi.org/10.31235/osf.io/dqe9b
,2017, Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite Population Scenarios, http://dx.doi.org/10.48550/arxiv.1707.04800
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