Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 129 - Advances in Graph Inference and Network Analysis
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313377
Title: Estimation and Inference in Latent Structure Random Graphs
Author(s): Avanti Athreya* and Minh Tang and Youngser Park and Carey Priebe
Companies: Johns Hopkins University and NC State University and Johns Hopkins University and Johns Hopkins University
Keywords: Latent structure random graphs; Efficient estimation; bilateral homology

We introduce the latent structure model (LSM) random graph, defined as a random dot product graph in which the latent position distribution incorporates both probabilistic and geometric constraints. To describe the latent position distribution, we specify both a family of underlying distributions on some fixed Euclidean space and a structural support submanifold from which the latent positions for the graph are drawn. Under mild conditions, we show how spectral estimates of the latent positions of an RDPG can be used for efficient estimation of the paramaters of the LSM, and we describe how to estimate or learn the structural support in cases where it is unknown, with an illustrative focus on graphs with latent positions along the Hardy-Weinberg curve. Finally, we use the latent structure model formulation to test bilateral homology in the Drosophila connectome.   

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

Back to the full JSM 2020 program