Inferring Latent Graphs from Stationary Signals Using a Graphical Autoregressive Model

TL;DR

This paper introduces the graphical autoregressive (GAR) model to infer latent graphs from stationary signals, extending autoregressive models to graph structures and demonstrating its effectiveness on financial data.

Contribution

The paper proposes a novel GAR framework for inferring latent graphs from stationary signals, supported by a three-step estimation procedure and theoretical analysis.

Findings

GAR outperforms Gaussian graphical models when well-fitted

Simulation studies validate the GAR model's effectiveness

Application to stock data demonstrates practical utility

Abstract

Graphs are an intuitive way to represent relationships between variables in fields such as finance and neuroscience. However, these graphs often need to be inferred from data. In this paper, we propose a novel framework to infer a latent graph by treating the observed multidimensional data as graph-referenced stationary signals. Specifically, we introduce the graphical autoregressive model (GAR), where the inverse covariance matrix of the observed signals is expressed as a second-order polynomial of the normalized graph Laplacian of the latent graph. The GAR model extends the autoregressive model from time series analysis to general undirected graphs, offering a new approach to graph inference. To estimate the latent graph, we develop a three-step procedure based on penalized maximum likelihood, supported by theoretical analysis and numerical experiments. Simulation studies and an application to S&P 500 stock price data show that the GAR model can outperform Gaussian graphical models when it fits the observed data well. Our results suggest that the GAR model offers a promising new direction for inferring latent graphs across diverse applications. Codes and example scripts are available at https://github.com/jed-harwood/SGM .