Estimating Gaussian graphical models from partial graphs

Paper Title:

Estimation of partially known Gaussian graphical models with score-based structural priors

Published on:

25 January 2024

Primary Category:

Machine Learning

Paper Authors:

Martín Sevilla,

Antonio García Marques,

Santiago Segarra

Bullets

Key Details

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Proposes novel way to estimate Gaussian graphical models using partial graph data

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Leverages graph neural networks to learn prior over graph structures

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Uses Langevin dynamics to sample from posterior over unknown graph edges

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Demonstrates improved performance over existing methods in experiments

Explore the topics in this paper

flexible priors

graph structure learning

graphical models

langevin dynamics

neural networks

AI generated summary

Estimating Gaussian graphical models from partial graphs

This paper proposes a new method to estimate the structure of Gaussian graphical models when part of the graph is already known. It uses graph neural networks and a sampling technique called Langevin dynamics to incorporate flexible prior information about graph structure from existing graph datasets. This allows it to outperform classical approaches in experiments.