502 citations · 1.3k across the 19 of their papers we have counts for
21 papers
Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systems
Saman Forouzandeh, Pavel N. Krivitsky, Rohitash Chandra
Recommender systems leveraging deep learning models have been crucial for assisting users in selecting items aligned with their preferences and interests. However, a significant ch…
Rejoinder to Discussion of "A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks''
Pavel N. Krivitsky, Pietro Coletti, Niel Hens
This rejoinder responds to discussions by of Caimo, Niezink, and Schweinberger and Fritz of ''A Tale of Two Datasets: Representativeness and Generalisability of Inference for Sampl…
Balance Correlations, Agentic Zeros, and Networks: The Structure of 192 Years of War and Peace
David Dekker, David Krackhardt, Patrick Doreian +1
Social network extensions of Heider's balance theory have not always been consistent. Structural balance theory primarily focuses on graph partitioning, thereby assuming, homogenei…
ergm 4: Computational Improvements
Pavel N. Krivitsky, David R. Hunter, Martina Morris +1
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…
Modeling Tie Duration in ERGM-Based Dynamic Network Models
Pavel N. Krivitsky
Krivitsky and Handcock (2014) proposed a Separable Temporal ERGM (STERGM) framework for modeling social networks, which facilitates separable modeling of the tie duration distribut…
Modeling of Dynamic Networks based on Egocentric Data with Durational Information
Pavel N. Krivitsky
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…
Likelihood-based Inference for Exponential-Family Random Graph Models via Linear Programming
Pavel N. Krivitsky, Alina R. Kuvelkar, David R. Hunter
This article discusses the problem of determining whether a given point, or set of points, lies within the convex hull of another set of points in dimensions. This problem aris…
A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks
Pavel N. Krivitsky, Pietro Coletti, Niel Hens
The last two decades have seen considerable progress in foundational aspects of statistical network analysis, but the path from theory to application is not straightforward. Two la…
ergm 4: New features
Pavel N. Krivitsky, David R. Hunter, Martina Morris +1
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…
Bayesian graph convolutional neural networks via tempered MCMC
Rohitash Chandra, Ayush Bhagat, Manavendra Maharana +1
Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks, particularly those with structured data. More recently, there ha…
Revisiting Bayesian Autoencoders with MCMC
Rohitash Chandra, Mahir Jain, Manavendra Maharana +1
Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent deep learning methods h…
Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite Population Scenarios
Michael Schweinberger, Pavel N. Krivitsky, Carter T. Butts +1
Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework for modeling sparse and dense random graphs, short- and long-tailed degree distributions, co…
A note on the role of projectivity in likelihood-based inference for random graph models
Michael Schweinberger, Pavel N. Krivitsky, Carter T. Butts
There is widespread confusion about the role of projectivity in likelihood-based inference for random graph models. The confusion is rooted in claims that projectivity, a form of m…
Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models
Vishesh Karwa, Pavel N. Krivitsky, Aleksandra B. Slavković
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…
Capturing Multivariate Spatial Dependence: Model, Estimate and then Predict
Noel Cressie, Sandy Burden, Walter Davis +4
Physical processes rarely occur in isolation, rather they influence and interact with one another. Thus, there is great benefit in modeling potential dependence between both spatia…
Differentially Private Exponential Random Graphs
Vishesh Karwa, Aleksandra B. Slavković, Pavel Krivitsky
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 fittin…
Exponential-Family Random Graph Models for Rank-Order Relational Data
Pavel N. Krivitsky, Carter T. Butts
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 interperson…
On the Question of Effective Sample Size in Network Modeling: An Asymptotic Inquiry
Pavel N. Krivitsky, Eric D. Kolaczyk
The modeling and analysis of networks and network data has seen an explosion of interest in recent years and represents an exciting direction for potential growth in statistics. De…
Exponential-Family Random Graph Models for Valued Networks
Pavel N. Krivitsky
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, m…
A Separable Model for Dynamic Networks
Pavel N. Krivitsky, Mark S. Handcock
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…
Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models
Pavel N. Krivitsky, Mark S. Handcock, Martina Morris
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 frien…