7 papers · 1 filter
Generalized Bayesian Inference for Dynamic Random Dot Product Graphs
Joshua Daniel Loyal
The random dot product graph is a popular model for network data with extensions that accommodate dynamic (time-varying) networks. However, two significant deficiencies exist in th…
A Latent Space Approach to Inferring Distance-Dependent Reciprocity in Directed Networks
Joshua Daniel Loyal, Xiangyu Wu, Jonathan R. Stewart
Reciprocity, or the stochastic tendency for actors to form mutual relationships, is an essential characteristic of directed network data. Existing latent space approaches to modeli…
Fast Variational Inference of Latent Space Models for Dynamic Networks Using Bayesian P-Splines
Joshua Daniel Loyal
Latent space models (LSMs) are often used to analyze dynamic (time-varying) networks that evolve in continuous time. Existing approaches to Bayesian inference for these models rely…
A Spike-and-Slab Prior for Dimension Selection in Generalized Linear Network Eigenmodels
Joshua Daniel Loyal, Yuguo Chen
Latent space models (LSMs) are frequently used to model network data by embedding a network's nodes into a low-dimensional latent space; however, choosing the dimension of this spa…
Dimension Reduction Forests: Local Variable Importance using Structured Random Forests
Joshua Daniel Loyal, Ruoqing Zhu, Yifan Cui +1
Random forests are one of the most popular machine learning methods due to their accuracy and variable importance assessment. However, random forests only provide variable importan…
An Eigenmodel for Dynamic Multilayer Networks
Joshua Daniel Loyal, Yuguo Chen
Dynamic multilayer networks frequently represent the structure of multiple co-evolving relations; however, statistical models are not well-developed for this prevalent network type…