% Encoding: UTF-8 @Article{KrMo17i, author = {Pavel N. Krivitsky and Martina Morris}, title = {Inference for Social Network Models from Egocentrically-sampled Data, with Application to Understanding Persistent Racial Disparities in {HIV} Prevalence in the {US}}, journal = {Annals of Applied Statistics}, year = {2017}, volume = {11}, number = {1}, pages = {427--455}, abstract = {Egocentric network sampling observes the network of interest from the point of view of a set of sampled actors, who provide information about themselves and anonymized information on their network neighbors. In survey research, this is often the most practical, and sometimes the only, way to observe certain classes of networks, with the sexual networks that underlie HIV transmission being the archetypal case. Although methods exist for recovering some descriptive network features, there is no rigorous and practical statistical foundation for estimation and inference for network models from such data. We identify a subclass of exponential-family random graph models (ERGMs) amenable to being estimated from egocentrically sampled network data, and apply pseudo-maximum-likelihood estimation to do so and to rigorously quantify the uncertainty of the estimates. For ERGMs parametrized to be invariant to network size, we describe a computationally tractable approach to this problem. We use this methodology to help understand persistent racial disparities in HIV prevalence in the US. We also discuss some extensions, including how our framework may be applied to triadic effects when data about ties among the respondent’s neighbors are also collected.}, doi = {10.1214/16-AOAS1010}, journaltitle = {Annals of Applied Statistics}, keywords = {social network; ERGM; random graph; egocentrically-sampled data; pseudo maximum likelihood; pseudo likelihood}, } @Article{Kr17u, author = {Pavel N. Krivitsky}, title = {Using Contrastive Divergence to Seed {Monte} {Carlo} {MLE} for Exponential-family Random Graph Models}, journal = {Computational Statistics \& Data Analysis}, year = {2017}, volume = {107}, pages = {149--161}, month = mar, doi = {10.1016/j.csda.2016.10.015}, } @Article{Kr12e, author = {Krivitsky, Pavel N.}, title = {Exponential-family Random Graph Models for Valued Networks}, journal = {Electronic Journal of Statistics}, year = {2012}, volume = {6}, pages = {1100--1128}, abstract = {Exponential-family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such as propensities for homophily, mutuality, and friend-of-a-friend triad closure, through choice of model terms (sufficient statistics). However, those ERGMs modeling the more complex features have, to date, been limited to binary data: presence or absence of ties. Thus, analysis of valued networks, such as those where counts, measurements, or ranks are observed, has necessitated dichotomizing them, losing information and introducing biases. In this work, we generalize ERGMs to valued networks. Focusing on modeling counts, we formulate an ERGM for networks whose ties are counts and discuss issues that arise when moving beyond the binary case. We introduce model terms that generalize and model common social network features for such data and apply these methods to a network dataset whose values are counts of interactions.}, doi = {10.1214/12-EJS696}, } @Article{KrKu23l, author = {Krivitsky, Pavel N. and Kuvelkar, Alina R. and Hunter, David R.}, title = {Likelihood-based Inference for Exponential-family Random Graph Models via Linear Programming}, journal = {Electronic Journal of Statistics}, year = {2023}, volume = {17}, number = {2}, month = jan, issn = {1935-7524}, doi = {10.1214/23-EJS2176}, publisher = {Institute of Mathematical Statistics}, } @Article{MaSu15i, author = {Mazur, Luke and Suesse, Thomas and Krivitsky, Pavel N.}, title = {Investigating Foreign Portfolio Investment Holdings: Gravity Model with Social Network Analysis}, journal = {International Journal of Finance \& Economics}, year = {2020}, volume = {27}, number = {1}, pages = {554--570}, month = sep, issn = {1099-1158}, doi = {10.1002/ijfe.2168}, publisher = {Wiley}, } @Article{CaKr15a, author = {Nicole Bohme Carnegie and Pavel N. Krivitsky and David R. Hunter and Steven M. Goodreau}, title = {An Approximation Method for Improving Dynamic Network Model Fitting}, journal = {Journal of Computational and Graphical Statistics}, year = {2015}, volume = {24}, number = {2}, pages = {502--519}, abstract = { There has been a great deal of interest recently in the modeling and simulation of dynamic networks, that is, networks that change over time. One promising model is the separable temporal exponential-family random graph model (ERGM) of Krivitsky and Handcock, which treats the formation and dissolution of ties in parallel at each time step as independent ERGMs. However, the computational cost of fitting these models can be substantial, particularly for large, sparse networks. Fitting cross-sectional models for observations of a network at a single point in time, while still a nonnegligible computational burden, is much easier. This article examines model fitting when the available data consist of independent measures of cross-sectional network structure and the duration of relationships under the assumption of stationarity. We introduce a simple approximation to the dynamic parameters for sparse networks with relationships of moderate or long duration and show that the approximation method works best in precisely those cases where parameter estimation is most likely to fail—networks with very little change at each time step. We consider a variety of cases: Bernoulli formation and dissolution of ties, independent-tie formation and Bernoulli dissolution, independent-tie formation and dissolution, and dependent-tie formation models. }, doi = {10.1080/10618600.2014.903087}, } @Article{HuKr12c, author = {Hunter, David R. and Krivitsky, Pavel N. and Schweinberger, Michael}, title = {Computational Statistical Methods for Social Network Models}, journal = {Journal of Computational and Graphical Statistics}, year = {2012}, volume = {21}, number = {4}, pages = {856--882}, abstract = {We review the broad range of recent statistical work in social network models, with emphasis on computational aspects of these methods. Particular focus is applied to exponential-family random graph models (ERGM) and latent variable models for data on complete networks observed at a single time point, though we also briefly review many methods for incompletely observed networks and networks observed at multiple time points. Although we mention far more modeling techniques than we can possibly cover in depth, we provide numerous citations to current literature. We illustrate several of the methods on a small, well-known network dataset, Sampson's monks, providing code where possible so that these analyses may be duplicated.}, doi = {10.1080/10618600.2012.732921}, keywords = {Degeneracy; ERGM; Latent variables; MCMC MLE; Variational methods}, publisher = {Taylor \& Francis}, } @Article{KrHa08f, author = {Krivitsky, Pavel N. and Handcock, Mark S.}, title = {Fitting Position Latent Cluster Models for Social Networks with \pkg{latentnet}}, journal = {Journal of Statistical Software}, year = {2008}, volume = {24}, number = {5}, pages = {1--23}, month = may, issn = {1548-7660}, abstract = {latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002) suggested an approach to modeling networks based on positing the existence of an latent space of characteristics of the actors. Relationships form as a function of distances between these characteristics as well as functions of observed dyadic level covariates. In latentnet social distances are represented in a Euclidean space. It also includes a variant of the extension of the latent position model to allow for clustering of the positions developed in Handcock, Raftery, and Tantrum (2007). The package implements Bayesian inference for the models based on an Markov chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position model and a two-stage maximum likelihood method for the latent position cluster model. For latent position cluster models, the package provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering (since if the preferred number of groups is 1, there is little evidence for clustering). It also estimates which cluster each actor belongs to. These estimates are probabilistic, and provide the probability of each actor belonging to each cluster. It computes four types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior mode and the estimator which minimizes Kullback-Leibler divergence from the posterior. You can assess the goodness-of-fit of the model via posterior predictive checks. It has a function to simulate networks from a latent position or latent position cluster model.}, doi = {10.18637/jss.v024.i05}, } @Article{KrHu23e, author = {Pavel N. Krivitsky and David R. Hunter and Martina Morris and Chad Klumb}, title = {{ergm} 4: New Features for Analyzing Exponential-Family Random Graph Models}, journal = {Journal of Statistical Software}, year = {2023}, volume = {105}, number = {6}, pages = {1--44}, doi = {10.18637/jss.v105.i06}, } @Article{KrCo23t, author = {Krivitsky, Pavel N. and Coletti, Pietro and Hens, Niel}, title = {A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks}, journal = {Journal of the American Statistical Association}, year = {2023}, pages = {1--21}, month = aug, doi = {10.1080/01621459.2023.2242627}, publisher = {Informa UK Limited}, } @Article{KrHa14s, author = {Krivitsky, Pavel N. and Handcock, Mark S.}, title = {A Separable Model for Dynamic Networks}, journal = {Journal of the Royal Statistical Society, Series B}, year = {2014}, volume = {76}, number = {1}, pages = {29--46}, abstract = {Models of dynamic networks --- networks that evolve over time --- have manifold applications. We develop a discrete-time generative model for social network evolution that inherits the richness and flexibility of the class of exponential-family random graph models. The model facilitates separable modeling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood-based inference for the model, and provide computational algorithms for maximum likelihood estimation. We illustrate the interpretability of the model in analyzing a longitudinal network of friendship ties within a school.}, doi = {10.1111/rssb.12014}, keywords = {Social networks; Longitudinal; Exponential random graph model; Markov chain Monte Carlo; Maximum likelihood estimation}, } @Article{KaKr17s, author = {Vishesh Karwa and Pavel N. Krivitsky and Aleksandra B. Slavkovi\'c}, title = {Sharing Social Network Data: Differentially Private Estimation of Exponential-family Random Graph Models}, journal = {Journal of the Royal Statistical Society, Series C}, year = {2017}, volume = {66}, number = {3}, pages = {481--500}, abstract = {Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. Two case studies demonstrate the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy \emph{and} supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analyzing such data. We use a simple yet effective randomized response mechanism to generate synthetic networks under $\epsilon$-edge differential privacy. We combine ideas and methods from both the statistics and the computer sciences, by utilizing likelihood based inference for missing data and Markov chain Monte Carlo (MCMC) techniques to fit exponential-family random graph models (ERGMs) to the generated synthetic networks.}, doi = {10.1111/rssc.12185}, } @Article{KrKo20e, author = {Pavel N. Krivitsky and Laura M. Koehly and Christopher Steven Marcum}, title = {Exponential-family Random Graph Models for Multi-layer Networks}, journal = {Psychometrika}, year = {2020}, volume = {85}, number = {3}, pages = {630--659}, month = sep, doi = {10.1007/s11336-020-09720-7}, publisher = {Springer Science and Business Media {LLC}}, } @Article{KrHa09r, author = {Krivitsky, Pavel N. and Handcock, Mark S. and Raftery, Adrian E. and Hoff, Peter D.}, title = {Representing Degree Distributions, Clustering, and Homophily in Social Networks with Latent Cluster Random Effects Models}, journal = {Social Networks}, year = {2009}, volume = {31}, number = {3}, pages = {204--213}, month = jul, issn = {0378-8733}, abstract = {Social network data often involve transitivity, homophily on observed attributes, community structure, and heterogeneity of actor degrees. We propose a latent cluster random effects model to represent all of these features, and we develop Bayesian inference for it. The model is applicable to both binary and non-binary network data. We illustrate the model using two real datasets: liking between monks and coreaderships between Slovenian publications. We also apply it to two simulated network datasets with very different network structure but the same highly skewed degree sequence generated from a preferential attachment process. One has transitivity and community structure while the other does not. Models based solely on degree distributions, such as scale-free, preferential attachment and power-law models, cannot distinguish between these very different situations, but the latent cluster random effects model does.}, doi = {10.1016/j.socnet.2009.04.001}, } @Article{KrMo21i, author = {Pavel N. Krivitsky and Martina Morris and Micha{\l} Bojanowski}, title = {Impact of Survey Design on Estimation of Exponential-family Random Graph Models from Egocentrically-sampled Data}, journal = {Social Networks}, year = {2021}, month = jun, doi = {10.1016/j.socnet.2020.10.001}, publisher = {Elsevier {BV}}, } @Article{KrBu17e, author = {Krivitsky, Pavel N. and Butts, Carter T.}, title = {Exponential-family Random Graph Models for Rank-order Relational Data}, journal = {Sociological Methodology}, year = {2017}, volume = {47}, number = {1}, pages = {68--112}, abstract = {Rank-order relational data, in which each actor ranks the others according to some criterion, often arise from sociometric measurements of judgment (e.g., self-reported interpersonal interaction) or preference (e.g., relative liking). We propose a class of exponential-family models for rank-order relational data and derive a new class of sufficient statistics for such data, which assume no more than within-subject ordinal properties. Application of MCMC MLE to this family allows us to estimate effects for a variety of plausible mechanisms governing rank structure in cross-sectional context, and to model the evolution of such structures over time. We apply this framework to model the evolution of relative liking judgments in an acquaintance process, and to model recall of relative volume of interpersonal interaction among members of a technology education program.}, doi = {10.1177/0081175017692623}, keywords = {ERGM; social networks; ranks; weighted networks; transitivity; mutuality}, } @Article{KrHa11a, author = {Krivitsky, Pavel N. and Handcock, Mark S. and Morris, Martina}, title = {Adjusting for Network Size and Composition Effects in Exponential-family Random Graph Models}, journal = {Statistical Methodology}, year = {2011}, volume = {8}, number = {4}, pages = {319--339}, month = jul, issn = {1572-3127}, abstract = {Exponential-family random graph models (ERGMs) provide a principled way to model and simulate features common in human social networks, such as propensities for homophily and friend-of-a-friend triad closure. We show that, without adjustment, ERGMs preserve density as network size increases. Density invariance is often not appropriate for social networks. We suggest a simple modification based on an offset which instead preserves the mean degree and accommodates changes in network composition asymptotically. We demonstrate that this approach allows ERGMs to be applied to the important situation of egocentrically sampled data. We analyze data from the National Health and Social Life Survey (NHSLS).}, doi = {10.1016/j.stamet.2011.01.005}, keywords = {network size; ERGM; random graph; egocentrically-sampled data}, } @Article{CrBu15c, author = {Noel Cressie and Sandy Burden and Walter Davis and Pavel N. Krivitsky and Payam Mokhtarian and Thomas Suesse and Andrew Zammit-Mangion}, title = {Capturing Multivariate Spatial Dependence: {Model,} Estimate, and Then Predict {(Discussion Paper)}}, journal = {Statistical Science}, year = {2015}, volume = {30}, number = {2}, pages = {170--175}, month = may, doi = {10.1214/15-STS517}, } @Article{KrKo15q, author = {Krivitsky, Pavel N. and Kolaczyk, Eric D.}, title = {On the Question of Effective Sample Size in Network Modeling: An Asymptotic Inquiry}, journal = {Statistical Science}, year = {2015}, volume = {30}, number = {2}, pages = {184--198}, month = may, doi = {10.1214/14-STS502}, fjournal = {Statistical Science}, publisher = {The Institute of Mathematical Statistics}, } @Article{ScKr20e, author = {Schweinberger, Michael and Krivitsky, Pavel N. and Butts, Carter T. and Stewart, Jonathan R.}, title = {Exponential-family Models of Random Graphs: {Inference} in Finite, Super and Infinite Population Scenarios}, journal = {Statistical Science}, year = {2020}, volume = {35}, number = {4}, pages = {627--662}, month = nov, doi = {10.1214/19-STS743}, fjournal = {Statistical Science}, publisher = {The Institute of Mathematical Statistics}, } @Article{KrCo23r, author = {Krivitsky, Pavel N. and Coletti, Pietro and Hens, Niel}, title = {Rejoinder to Discussion of “{A} Tale of Two Datasets: {Representativeness} and Generalisability of Inference for Samples of Networks”}, journal = {Journal of the American Statistical Association}, year = {2023}, volume = {118}, number = {544}, pages = {2235--2238}, month = oct, issn = {1537-274X}, doi = {10.1080/01621459.2023.2280383}, publisher = {Informa UK Limited}, url = {https://arxiv.org/abs/2312.06028}, } @InProceedings{KaSl14d, author = {Karwa, Vishesh and Slavkovi{\'c}, Aleksandra B and Krivitsky, Pavel}, title = {Differentially Private Exponential Random Graphs}, booktitle = {Privacy in Statistical Databases}, year = {2014}, editor = {Josep Domingo-Ferrer}, volume = {8744}, series = {Lecture Notes in Computer Science}, pages = {143--155}, publisher = {Springer International Publishing}, abstract = {We propose methods to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network. Proposed techniques aim at fitting and estimating a wide class of exponential random graph models (ERGMs) in a differentially private manner, and thus offer rigorous privacy guarantees. More specifically, we use the randomized response mechanism to release networks under ε-edge differential privacy. To maintain utility for statistical inference, treating the original graph as missing, we propose a way to use likelihood based inference and Markov chain Monte Carlo (MCMC) techniques to fit ERGMs to the produced synthetic networks. We demonstrate the usefulness of the proposed techniques on a real data example.}, doi = {10.1007/978-3-319-11257-2_12}, keywords = {Exponential random graphs; edge differential privacy; missing data; synthetic graphs}, } @Article{LeCl24c, author = {Leaver, Victoria L. and Clark, Robert G. and Krivitsky, Pavel N. and Birrell, Carole L.}, title = {A comparison of likelihood-based methods for size-biased sampling}, journal = {Journal of Statistical Planning and Inference}, year = {2024}, volume = {230}, pages = {106115}, month = may, issn = {0378-3758}, doi = {10.1016/j.jspi.2023.106115}, publisher = {Elsevier BV}, } @Article{ChBh21b, author = {Chandra, Rohitash and Bhagat, Ayush and Maharana, Manavendra and Krivitsky, Pavel N.}, title = {Bayesian Graph Convolutional Neural Networks via Tempered MCMC}, journal = {IEEE Access}, year = {2021}, volume = {9}, pages = {130353--130365}, issn = {2169-3536}, doi = {10.1109/access.2021.3111898}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, } @Article{ChJa22r, author = {Chandra, Rohitash and Jain, Mahir and Maharana, Manavendra and Krivitsky, Pavel N.}, title = {Revisiting Bayesian Autoencoders With MCMC}, journal = {IEEE Access}, year = {2022}, volume = {10}, pages = {40482--40495}, issn = {2169-3536}, doi = {10.1109/access.2022.3163270}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, } @InProceedings{BaKr21r, author = {Marijka Batterham and Pavel N. Krivitsky}, title = {Relationship Between Statistics Anxiety and Final Marks in an Introductory Biostatistics in Undergraduate Health Sciences Students}, booktitle = {OZCOTS 2021: Proceedings of the 10th Australian Conference on Teaching Statistics}, year = {2021}, url = {https://iase-web.org/documents/ANZCOTS/OZCOTS_2021_Proceedings.pdf}, } @InProceedings{LiKr18r, author = {Lin, Yan-Xia and Krivitsky, Pavel N.}, title = {Reviewing the Methods of Estimating the Density Function Based on Masked Data}, booktitle = {Privacy in Statistical Databases}, year = {2018}, volume = {11126}, series = {Lecture Notes in Computer Science}, pages = {231--246}, doi = {10.1007/978-3-319-99771-1_16}, } @InProceedings{MaLi18q, author = {Ma, Yue and Lin, Yan-Xia and Krivitsky, Pavel N. and Wakefield, Bradley}, title = {Quantifying Protection Level of a Noise Candidate for Noise Multiplication Masking Scheme}, booktitle = {Privacy in Statistical Databases}, year = {2018}, volume = {11126}, series = {Lecture Notes in Computer Science}, pages = {279--293}, doi = {10.1007/978-3-319-99771-1_19}, } @InProceedings{KrFe11n, author = {Pavel N. Krivitsky and Pedro M. A. Ferreira and Rahul Telang}, title = {Network Neighbor Effects on Customer Churn in Cell Phone Networks}, booktitle = {Proceedings of the 7th Symposium on Statistical Challenges in E-Commerce Research (SCECR 2011)}, year = {2011}, url = {https://ro.uow.edu.au/articles/conference_contribution/Network_neighbor_effects_on_customer_churn_in_cell_phone_networks/27694986}, } @InProceedings{RaNe07e, author = {Raftery, Adrian E. and Newton, Michael A. and Satagopan, Jaya M. and Krivitsky, Pavel N.}, title = {Estimating the Integrated Likelihood Via Posterior Simulation Using the Harmonic Mean Identity}, booktitle = {Bayesian Statistics 8: Proceedings of the Valencia/ISBA 8th World Meeting on Bayesian Statistics}, year = {2007}, volume = {8}, pages = {317--416}, publisher = {Oxford University Press, USA}, abstract = {The integrated likelihood (also called the marginal likelihood or the normalizing constant) is a central quantity in Bayesian model selection and model averaging. It is defined as the integral over the parameter space of the likelihood times the prior density. The Bayes factor for model comparison and Bayesian testing is a ratio of integrated likelihoods, and the model weights in Bayesian model averaging are proportional to the integrated likelihoods. We consider the estimation of the integrated likelihood from posterior simulation output, aiming at a generic method that uses only the likelihoods from the posterior simulation iterations. The key is the harmonic mean identity, which says that the reciprocal of the integrated likelihood is equal to the posterior harmonic mean of the likelihood. The simplest estimator based on the identity is thus the harmonic mean of the likelihoods. While this is an unbiased and simulation-consistent estimator, its reciprocal can have infinite variance and so it is unstable in general. We describe two methods for stabilizing the harmonic mean estimator. In the first one, the parameter space is reduced in such a way that the modified estimator involves a harmonic mean of heavier-tailed densities, thus resulting in a finite variance estimator. The resulting estimator is stable. It is also self-monitoring, since it obeys the central limit theorem, and so confidence intervals are available. We discuss general conditions under which this reduction is applicable. The second method is based on the fact that the posterior distribution of the log-likelihood is approximately a gamma distribution. This leads to an estimator of the maximum achievable likelihood, and also an estimator of the effective number of parameters that is extremely simple to compute from the loglikelihoods, independent of the model parametrization, and always positive. This yields estimates of the log integrated likelihood, and posterior simulation-based analogues of the BIC and AIC model selection criteria, called BICM and AICM. We illustrate the proposed methods through several examples.}, doi = {10.1093/oso/9780199214655.003.0015}, isbn = {0199214654}, } @TechReport{KrBo19i, author = {Pavel N. Krivitsky and Micha{{\l}} Bojanowski and Martina Morris}, title = {Inference for Exponential-family Random Graph Models from Egocentrically-sampled Data with Alter--alter Relations}, year = {2019}, url = {https://www.uow.edu.au/niasra/publications/}, } @Article{DeKr24b, author = {Dekker, David and Krackhardt, David and Doreian, Patrick and Krivitsky, Pavel N.}, title = {Balance correlations, agentic zeros, and networks: {The} structure of 192 years of war and peace}, journal = {PLOS ONE}, year = {2024}, volume = {19}, number = {12}, pages = {1--32}, month = {12}, doi = {10.1371/journal.pone.0315088}, publisher = {Public Library of Science}, } @Article{FoCh25m, author = {Forouzandeh, Saman and Krivitsky, Pavel N. and Chandra, Rohitash}, title = {Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systems}, journal = {Expert Systems with Applications}, year = {2025}, volume = {291}, pages = {128378}, month = oct, issn = {0957-4174}, abstract = {Recommender systems leveraging deep learning models have been crucial for assisting users in selecting items aligned with their preferences and interests. However, a significant challenge persists in single-criteria recommender systems, which often overlook the diverse attributes of items that have been addressed by Multi-Criteria Recommender Systems (MCRS). Shared embedding vector for multi-criteria item ratings but have struggled to capture the nuanced relationships between users and items based on specific criteria. In this study, we present a novel representation for Multi-Criteria Recommender Systems (MCRS) based on a multi-edge bipartite graph, where each edge represents one criterion rating of items by users, and Multiview Dual Graph Attention Networks (MDGAT). Employing MDGAT is beneficial and important for adequately considering all relations between users and items, given the presence of both local (criterion-based) and global (multi-criteria) relations. Additionally, we define anchor points in each view based on similarity and employ local and global contrastive learning to distinguish between positive and negative samples across each view and the entire graph. We evaluate our method on two real-world datasets and assess its performance based on item rating predictions. The results demonstrate that our method achieves higher accuracy compared to the baseline method for predicting item ratings on the same datasets. MDGAT effectively capture the local and global impact of neighbours and the similarity between nodes.}, doi = {10.1016/j.eswa.2025.128378}, publisher = {Elsevier BV}, } @TechReport{Kr12m, author = {Krivitsky, Pavel N.}, title = {Modeling of Dynamic Networks Based on Egocentric Data with Durational Information}, institution = {Pennsylvania State University Department of Statistics}, year = {2012}, number = {2012-01}, month = apr, abstract = {Modeling of dynamic networks — networks that evolve over time — has manifold applications in many fields. In epidemiology in particular, there is a need for data-driven modeling of human sexual relationship networks for the purpose of modeling and simulation of the spread of sexually transmitted disease. Dynamic network data about such networks are extremely difficult to collect, however, and much more readily available are egocentrically sampled data of a network at a single time point, with some attendant information about the sexual history of respondents. Krivitsky and Handcock (2010) proposed a Separable Temporal ERGM (STERGM) framework, which facilitates separable modeling of the tie duration distributions and the structural dynamics of tie formation. In this work, we apply this modeling framework to this problem, by studying the long-run properties of STERGM processes, developing methods for fitting STERGMs to egocentrically sampled data, and extending the network size adjustment method of Krivitsky, Handcock, and Morris (2011) to dynamic models.}, eprint = {2203.06866}, eprinttype = {arXiv}, url = {https://arxiv.org/abs/2203.06866}, } @TechReport{Kr12ma, author = {Krivitsky, Pavel N.}, title = {Modeling Tie Duration in {ERGM}-based Dynamic Network Models}, institution = {Pennsylvania State University Department of Statistics}, year = {2012}, number = {2012-02}, month = apr, abstract = {Krivitsky and Handcock (2010) proposed a Separable Temporal ERGM (STERGM) framework for modeling social networks, which facilitates separable modeling of the tie duration distributions and the structural dynamics of tie formation. In this note, we explore the hazard structures achievable in this framework, with first- and higher-order Markov assumptions, and propose ways to model a variety of duration distributions in this framework.}, eprint = {2203.11817}, eprinttype = {arXiv}, url = {https://arxiv.org/abs/2203.11817}, } @TechReport{KrHu22e, author = {Pavel N. Krivitsky and David R. Hunter and Martina Morris and Chad Klumb}, title = {{ergm} 4: Computational Improvements}, year = {2022}, abstract = {The ergm package supports the statistical analysis and simulation of network data. It anchors the statnet suite of packages for network analysis in R introduced in a special issue in Journal of Statistical Software in 2008. This article provides an overview of the performance improvements in the 2021 release of ergm version 4. These include performance enhancements to the Markov chain Monte Carlo and maximum likelihood estimation algorithms as well as broader and faster searching for networks with certain target statistics using simulated annealing.}, date = {2022-03-15}, eprint = {2203.08198}, eprintclass = {stat.CO}, eprinttype = {arXiv}, keywords = {stat.CO}, url = {https://arxiv.org/abs/2203.08198}, } @Article{FoKr25c, author = {Forouzandeh, Saman and Krivitsky, Pavel N. and Chandra, Rohitash}, title = {A comprehensive survey of modern recommendation systems: methods, personalisation and scalable deployment}, year = {2025}, month = oct, doi = {10.36227/techrxiv.176054814.40957359/v1}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, } @Comment{jabref-meta: databaseType:bibtex;}