Online Program Home
My Program

Abstract Details

Activity Number: 248
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319436 View Presentation
Title: Modeling Bipartite Graph Using Dependent Indian Buffet Processes
Author(s): Ketong Wang* and Michael D. Porter
Companies: University of Alabama and University of Alabama
Keywords: Bipartite Graph ; Latent Feature Model ; Dependent Indian Buffet Processes

In this paper, we propose a bipartite graph modeling framework using latent features generated by dependent Indian Buffet Processes (dIBP). The model not only preserves the properties brought by marginal IBPs, but also accommodates the dependence between the two nodal regimes of the bipartite structure through a correlated feature generating process. We provide an MCMC inference algorithm and illustrate the enriched modeling capabilities through graph link prediction and graph co-clustering.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association