By Dr Pavel Krivitsky

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

Chandra R; Jain M; Maharana M; Krivitsky PN, 2021, Revisiting Bayesian Autoencoders with MCMC, http://dx.doi.org/10.48550/arxiv.2104.05915

Krivitsky PN; Koehly L; Marcum CS, 2019, Exponential-Family Random Graph Models for Multi-Layer Networks, http://dx.doi.org/10.31235/osf.io/dqe9b

Schweinberger M; Krivitsky PN; Butts CT; Stewart J, 2017, Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite Population Scenarios, http://dx.doi.org/10.48550/arxiv.1707.04800

Schweinberger M; Krivitsky PN; Butts CT, 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

Karwa V; Krivitsky PN; Slavković AB, 2015, Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models, http://dx.doi.org/10.48550/arxiv.1511.02930

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, http://dx.doi.org/10.48550/arxiv.1507.08401

Karwa V; Slavković AB; Krivitsky P, 2014, Differentially Private Exponential Random Graphs, http://dx.doi.org/10.48550/arxiv.1409.4696

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

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

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

Krivitsky PN; Handcock MS, 2010, A Separable Model for Dynamic Networks, http://dx.doi.org/10.48550/arxiv.1011.1937

Krivitsky PN; Handcock MS; Morris M, 2010, Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models, http://dx.doi.org/10.48550/arxiv.1004.5328

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