Learning Graphs from Smooth and Graph-Stationary Signals with Hidden Variables

TL;DR

This paper develops methods for inferring network topology from nodal signals that are smooth and stationary, explicitly accounting for hidden variables, and demonstrates improved performance over existing approaches.

Contribution

It introduces a novel framework for network inference that considers hidden variables and leverages graph signal processing models with convex relaxations.

Findings

Effective inference of network topology with hidden variables.

Superior performance compared to classical correlation-based methods.

Validated on synthetic and real-world datasets.

Abstract

Network-topology inference from (vertex) signal observations is a prominent problem across data-science and engineering disciplines. Most existing schemes assume that observations from all nodes are available, but in many practical environments, only a subset of nodes is accessible. A natural (and sometimes effective) approach is to disregard the role of unobserved nodes, but this ignores latent network effects, deteriorating the quality of the estimated graph. Differently, this paper investigates the problem of inferring the topology of a network from nodal observations while taking into account the presence of hidden (latent) variables. Our schemes assume the number of observed nodes is considerably larger than the number of hidden variables and build on recent graph signal processing models to relate the signals and the underlying graph. Specifically, we go beyond classical correlation and partial correlation approaches and assume that the signals are smooth and/or stationary in the sought graph. The assumptions are codified into different constrained optimization problems, with the presence of hidden variables being explicitly taken into account. Since the resulting problems are ill-conditioned and non-convex, the block matrix structure of the proposed formulations is leveraged and suitable convex-regularized relaxations are presented. Numerical experiments over synthetic and real-world datasets showcase the performance of the developed methods and compare them with existing alternatives.