Time-evolving modeling of social networks

Journal Article

A statistical framework for modeling and prediction of binary matrices is presented. The method is applied to social network analysis, specifically the database of US Supreme Court rulings. It is shown that the ruling behavior of Supreme Court judges can be accurately modeled by using a small number of latent features whose values evolve with time. The learned model facilitates the discovery of inter-relationships between judges and of the gradual evolution of their stances over time. In addition, the analysis in this paper extends previous results by considering automatic estimation of the number of latent features and other model parameters, based on a nonparametric-Bayesian approach. Inference is efficiently performed using Gibbs sampling. © 2011 IEEE.

Full Text

Duke Authors

Cited Authors

  • Wang, E; Silva, J; Willett, R; Carin, L

Published Date

  • August 18, 2011

Published In

Start / End Page

  • 2184 - 2187

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2011.5946761

Citation Source

  • Scopus