Senior Lecturer

Dr Pavel Krivitsky

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

Journal articles | 2023

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

Journal articles | 2022

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

Journal articles | 2022

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

Journal articles | 2022

2022, 'Revisiting Bayesian Autoencoders With MCMC', IEEE Access, 10, pp. 40482 - 40495, http://dx.doi.org/10.1109/ACCESS.2022.3163270

Journal articles | 2021

2021, 'Bayesian Graph Convolutional Neural Networks via Tempered MCMC', IEEE Access, 9, pp. 130353 - 130365, http://dx.doi.org/10.1109/ACCESS.2021.3111898

Journal articles | 2020

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

Journal articles | 2020

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

Journal articles | 2017

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

Journal articles | 2017

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

Journal articles | 2017

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

Journal articles | 2017

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

Journal articles | 2015

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

Journal articles | 2015

2015, 'Capturing multivariate spatial dependence: Model, estimate and then predict', Statistical Science, 30, pp. 170 - 175, http://dx.doi.org/10.1214/15-STS517

Journal articles | 2015

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

Journal articles | 2014

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

Journal articles | 2012

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

Journal articles | 2012

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

Journal articles | 2011

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

Journal articles | 2009

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

Journal articles | 2008

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
Preprints | 2022

2022, A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks, , http://dx.doi.org/10.48550/arxiv.2202.03685

Preprints | 2022

2022, Likelihood-based Inference for Exponential-Family Random Graph Models via Linear Programming, , http://dx.doi.org/10.48550/arxiv.2202.03572

Preprints | 2022

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

Preprints | 2022

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

Preprints | 2022

2022, ergm 4: Computational Improvements, , http://dx.doi.org/10.48550/arxiv.2203.08198

Software / Code | 2022

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

Preprints | 2021

2021, Bayesian graph convolutional neural networks via tempered MCMC, , http://dx.doi.org/10.48550/arxiv.2104.08438

Preprints | 2021

2021, Revisiting Bayesian Autoencoders with MCMC, , http://dx.doi.org/10.48550/arxiv.2104.05915

Preprints | 2021

2021, ergm 4: New features, , http://dx.doi.org/10.48550/arxiv.2106.04997

Preprints | 2019

2019, Exponential-Family Random Graph Models for Multi-Layer Networks, , http://dx.doi.org/10.31235/osf.io/dqe9b

Software / Code | 2019

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

Software / Code | 2019

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

Software / Code | 2019

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

Software / Code | 2019

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

Software / Code | 2019

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

Software / Code | 2019

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

Software / Code | 2019

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

Software / Code | 2019

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

Conference Papers | 2018

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), pp. 279 - 293, http://dx.doi.org/10.1007/978-3-319-99771-1_19

Conference Papers | 2018

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), pp. 231 - 246, http://dx.doi.org/10.1007/978-3-319-99771-1_16

Software / Code | 2018

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 | 2017

2017, A note on the role of projectivity in likelihood-based inference for random graph models, , http://dx.doi.org/10.48550/arxiv.1707.00211

Preprints | 2017

2017, Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite Population Scenarios, , http://dx.doi.org/10.48550/arxiv.1707.04800

Preprints | 2015

2015, Capturing Multivariate Spatial Dependence: Model, Estimate and then Predict, , http://dx.doi.org/10.48550/arxiv.1507.08401

Preprints | 2015

2015, Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models, , http://dx.doi.org/10.48550/arxiv.1511.02930

Conference Papers | 2014

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

Preprints | 2014

2014, Differentially Private Exponential Random Graphs, , http://dx.doi.org/10.48550/arxiv.1409.4696

Preprints | 2012

2012, Exponential-Family Random Graph Models for Rank-Order Relational Data, , http://dx.doi.org/10.48550/arxiv.1210.0493

Preprints | 2011

2011, Exponential-Family Random Graph Models for Valued Networks, , http://dx.doi.org/10.48550/arxiv.1101.1359

Preprints | 2011

2011, On the Question of Effective Sample Size in Network Modeling: An Asymptotic Inquiry, , http://dx.doi.org/10.48550/arxiv.1112.0840

Preprints | 2010

2010, A Separable Model for Dynamic Networks, , http://dx.doi.org/10.48550/arxiv.1011.1937

Preprints | 2010

2010, Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models, , http://dx.doi.org/10.48550/arxiv.1004.5328

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

Statnet Project 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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