4 citations · 11 across the 4 of their papers we have counts for
5 papers
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…
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…
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…