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Dr Pavel Krivitsky

Dr Pavel Krivitsky

Senior Lecturer
  • 2009 PhD in Statistics University of Washington, Seattle, WA, USA
  • 2006 MS in Statistics University of Washington, Seattle, WA, USA
  • 2003 BS in Biometry and Statistics, Cornell University, Ithaca, NY, USA
Science
School of Mathematics & Statistics

I develop methods and models for analysing complex network data and processes with applications in epidemiology and the social sciences. I am particularly interested in unusual or indirectly observed or sampled network data. My work has a strong computational component, and I develop and maintain a number of popular R packages on CRAN.
Phone
9385 7022
E-mail
p.krivitsky@unsw.edu.au
Location
School of Mathematics and Statistics UNSW Sydney NSW 2052 The Red Centre Room 1032
  • Book Chapters | 2007
    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,
  • Journal articles | 2024
    Krivitsky PN; Coletti P; Hens N, 2024, 'Correction', Journal of the American Statistical Association, 119, pp. 1694 - 1695,
    Journal articles | 2023
    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, pp. 1 - 11,
    Journal articles | 2023
    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,
    Journal articles | 2022
    Chandra R; Jain M; Maharana M; Krivitsky PN, 2022, 'Revisiting Bayesian Autoencoders With MCMC', IEEE Access, 10, pp. 40482 - 40495,
    Journal articles | 2022
    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,
    Journal articles | 2022
    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,
    Journal articles | 2021
    Chandra R; Bhagat A; Maharana M; Krivitsky PN, 2021, 'Bayesian Graph Convolutional Neural Networks via Tempered MCMC', IEEE Access, 9, pp. 130353 - 130365,
    Journal articles | 2020
    Krivitsky PN; Koehly LM; Marcum CS, 2020, 'Exponential-Family Random Graph Models for Multi-Layer Networks', Psychometrika, 85, pp. 630 - 659,
    Journal articles | 2020
    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,
    Journal articles | 2017
    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,
    Journal articles | 2017
    Krivitsky PN; Butts CT, 2017, 'Exponential-family random graph models for rank-order relational data', Sociological Methodology, 47, pp. 68 - 112,
    Journal articles | 2017
    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,
    Journal articles | 2017
    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,
    Journal articles | 2015
    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,
    Journal articles | 2015
    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,
    Journal articles | 2015
    Krivitsky PN; Kolaczyk ED, 2015, 'On the question of effective sample size in network modeling: An asymptotic inquiry', Statistical Science, 30, pp. 184 - 198,
    Journal articles | 2014
    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,
    Journal articles | 2012
    Hunter DR; Krivitsky PN; Schweinberger M, 2012, 'Computational statistical methods for social network models', Journal of Computational and Graphical Statistics, 21, pp. 856 - 882,
    Journal articles | 2012
    Krivitsky PN, 2012, 'Exponential-family random graph models for valued networks', Electronic Journal of Statistics, 6, pp. 1100 - 1128,
    Journal articles | 2011
    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,
    Journal articles | 2009
    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,
    Journal articles | 2008
    Krivitsky PN; Handcock MS, 2008, 'Fitting position latent cluster models for social networks with latentnet', Journal of Statistical Software, 24,
  • Preprints | 2023
    Dekker D; Krackhardt D; Doreian P; Krivitsky PN, 2023, Balance Correlations, Agentic Zeros, and Networks: The Structure of 192 Years of War and Peace,
    Preprints | 2022
    Krivitsky PN; Coletti P; Hens N, 2022, A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks, ,
    Preprints | 2022
    Krivitsky PN; Hunter DR; Morris M; Klumb C, 2022, ergm 4: Computational Improvements, ,
    Preprints | 2022
    Krivitsky PN; Kuvelkar AR; Hunter DR, 2022, Likelihood-based Inference for Exponential-Family Random Graph Models via Linear Programming, ,
    Preprints | 2022
    Krivitsky PN, 2022, Modeling Tie Duration in ERGM-Based Dynamic Network Models, ,
    Preprints | 2022
    Krivitsky PN, 2022, Modeling of Dynamic Networks based on Egocentric Data with Durational Information, ,
    Software / Code | 2022
    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,
    Preprints | 2021
    Chandra R; Bhagat A; Maharana M; Krivitsky PN, 2021, Bayesian graph convolutional neural networks via tempered MCMC, ,
    Preprints | 2021
    Chandra R; Jain M; Maharana M; Krivitsky PN, 2021, Revisiting Bayesian Autoencoders with MCMC,
    Preprints | 2021
    Krivitsky PN; Hunter DR; Morris M; Klumb C, 2021, ergm 4: New features,
    Software / Code | 2019
    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,
    Software / Code | 2019
    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,
    Software / Code | 2019
    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,
    Software / Code | 2019
    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,
    Preprints | 2019
    Krivitsky PN; Koehly L; Marcum CS, 2019, Exponential-Family Random Graph Models for Multi-Layer Networks,
    Software / Code | 2019
    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,
    Software / Code | 2019
    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,
    Software / Code | 2019
    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,
    Software / Code | 2019
    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,
    Software / Code | 2018
    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,
    Conference Papers | 2018
    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,
    Conference Papers | 2018
    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,
    Preprints | 2017
    Schweinberger M; Krivitsky PN; Butts CT; Stewart J, 2017, Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite Population Scenarios, ,
    Preprints | 2017
    Schweinberger M; Krivitsky PN; Butts CT, 2017, A note on the role of projectivity in likelihood-based inference for random graph models, ,
    Preprints | 2015
    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, ,
    Preprints | 2015
    Karwa V; Krivitsky PN; Slavković AB, 2015, Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models, ,
    Conference Papers | 2014
    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,
    Preprints | 2014
    Karwa V; Slavković AB; Krivitsky P, 2014, Differentially Private Exponential Random Graphs, ,
    Preprints | 2012
    Krivitsky PN; Butts CT, 2012, Exponential-Family Random Graph Models for Rank-Order Relational Data, ,
    Preprints | 2011
    Krivitsky PN; Kolaczyk ED, 2011, On the Question of Effective Sample Size in Network Modeling: An Asymptotic Inquiry, ,
    Preprints | 2011
    Krivitsky PN, 2011, Exponential-Family Random Graph Models for Valued Networks, ,
    Preprints | 2010
    Krivitsky PN; Handcock MS; Morris M, 2010, Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models, ,
    Preprints | 2010
    Krivitsky PN; Handcock MS, 2010, A Separable Model for Dynamic Networks, ,

  • 2019 for significant contributions to the scientific study of social structure by an early career researcher.
  • 2019 for development of publicly social network analysis software.

  • to produce user-friendly open source software tools for network analysis
  • uDASH: UNSW Data Science Hub

My Research Supervision

  • Saman Forouzandeh 2021‒
  • Yun Pan 2022‒

My Teaching

  • MATH3831 (Statistics in Social and Market Research)
  • DATA5002 (Data Visualisation)
  • MATH5855/ZZSC5855 (Multivariate Analysis)
  • DATA1001 (Fundamentals of Data Science)

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