Time-evolving modeling of social networks

Eric Wang, Jorge Silva, Rebecca Willett, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages2184-2187
Number of pages4
DOIs
StatePublished - Aug 18 2011
Externally publishedYes

Fingerprint Dive into the research topics of 'Time-evolving modeling of social networks'. Together they form a unique fingerprint.

Cite this