Latent Space Models for Dynamic Networks

TL;DR

This paper introduces a latent space model for dynamic networks that visualizes and analyzes the evolution of relationships over time, with applications to real-world social and political networks.

Contribution

It presents a novel latent space approach with an MCMC estimation method for dynamic networks, including visualization and influence detection capabilities.

Findings

Effective visualization of network evolution

Successful application to real-world datasets

Ability to predict future network edges

Abstract

Dynamic networks are used in a variety of fields to represent the structure and evolution of the relationships between entities. We present a model which embeds longitudinal network data as trajectories in a latent Euclidean space. A Markov chain Monte Carlo algorithm is proposed to estimate the model parameters and latent positions of the actors in the network. The model yields meaningful visualization of dynamic networks, giving the researcher insight into the evolution and the structure, both local and global, of the network. The model handles directed or undirected edges, easily handles missing edges, and lends itself well to predicting future edges. Further, a novel approach is given to detect and visualize an attracting influence between actors using only the edge information. We use the case-control likelihood approximation to speed up the estimation algorithm, modifying it slightly to account for missing data. We apply the latent space model to data collected from a Dutch classroom, and a cosponsorship network collected on members of the U.S. House of Representatives, illustrating the usefulness of the model by making insights into the networks.