Grunnleggende konsepter
Generating graphs with fidelity and diversity using SteinGen.
Sammendrag
The content discusses the challenges of graph generation and introduces SteinGen, a novel method based on Glauber dynamics. It addresses the issues of fidelity and diversity in generating graph samples from a single observed graph. The article provides theoretical guarantees, stability analysis, and measures of sample fidelity using total variation distance.
- Introduction to challenges in graph generation.
- Overview of SteinGen and its methodology.
- Theoretical analysis on consistency, diversity, mixing time, and stability.
- Measurement of sample fidelity using total variation distance.
Statistikk
"Generating graphs that preserve characteristic structures while promoting sample diversity can be challenging."
"The classical approach of graph generation from parametric models relies on the estimation of parameters."
"Our proposed generating procedure, SteinGen, combines ideas from Stein’s method and MCMC."
"SteinGen uses the Glauber dynamics associated with an estimated Stein operator to generate a sample."
"We show that on a class of exponential random graph models this novel 'estimation and re-estimation' generation strategy yields high distributional similarity to the original data, combined with high sample diversity."
Sitater
"Synthetic data generation is a key ingredient for many modern statistics and machine learning tasks."
"Graph generation based on representation learning and augmentation have also been considered."
"The total variation distance between empirical degree distributions is used as a measure of sample fidelity."