14 citations · 16 across the 5 of their papers we have counts for
4 papers · 1 filter
Model-Based Inference and Experimental Design for Interference Using Partial Network Data
Steven Wilkins Reeves, Shane Lubold, Arun G. Chandrasekhar +1
The stable unit treatment value assumption states that the outcome of an individual is not affected by the treatment statuses of others, however in many real world applications, tr…
Bayesian Hyperbolic Multidimensional Scaling
Bolun Liu, Shane Lubold, Adrian E. Raftery +1
Multidimensional scaling (MDS) is a widely used approach to representing high-dimensional, dependent data. MDS works by assigning each observation a location on a low-dimensional g…
Spectral goodness-of-fit tests for complete and partial network data
Shane Lubold, Bolun Liu, Tyler H. McCormick
Networks describe the, often complex, relationships between individual actors. In this work, we address the question of how to determine whether a parametric model, such as a stoch…
Identifying the latent space geometry of network models through analysis of curvature
Shane Lubold, Arun G. Chandrasekhar, Tyler H. McCormick
A common approach to modeling networks assigns each node to a position on a low-dimensional manifold where distance is inversely proportional to connection likelihood. More positiv…