Joint Inference of Multiple Graphs from Matrix Polynomials

TL;DR

This paper introduces a convex optimization approach for jointly inferring multiple stationary graphs from observed signals, providing theoretical guarantees and demonstrating effectiveness on synthetic and real data.

Contribution

It develops a novel convex method for joint graph inference from multiple signals, leveraging matrix polynomial properties and offering recovery guarantees.

Findings

Successful recovery of true graphs with perfect covariance information

Robustness of the method in noisy conditions

High-probability bounds on recovery error

Abstract

Inferring graph structure from observations on the nodes is an important and popular network science task. Departing from the more common inference of a single graph and motivated by social and biological networks, we study the problem of jointly inferring multiple graphs from the observation of signals at their nodes (graph signals), which are assumed to be stationary in the sought graphs. From a mathematical point of view, graph stationarity implies that the mapping between the covariance of the signals and the sparse matrix representing the underlying graph is given by a matrix polynomial. A prominent example is that of Markov random fields, where the inverse of the covariance yields the sparse matrix of interest. From a modeling perspective, stationary graph signals can be used to model linear network processes evolving on a set of (not necessarily known) networks. Leveraging that matrix polynomials commute, a convex optimization method along with sufficient conditions that guarantee the recovery of the true graphs are provided when perfect covariance information is available. Particularly important from an empirical viewpoint, we provide high-probability bounds on the recovery error as a function of the number of signals observed and other key problem parameters. Numerical experiments using synthetic and real-world data demonstrate the effectiveness of the proposed method with perfect covariance information as well as its robustness in the noisy regime.