Learning Time-Varying Graphs from Incomplete Graph Signals

TL;DR

This paper introduces a unified framework for inferring time-varying network structures and imputing missing graph signal data, using a non-convex optimization approach with theoretical guarantees and efficient algorithms.

Contribution

It proposes a novel joint optimization method with a fused-lasso regularizer and a scalable PADMM algorithm, providing convergence and statistical guarantees.

Findings

Outperforms state-of-the-art methods in accuracy and speed.

Effectively captures gradual network evolution with temporal regularization.

Demonstrates robustness in high missing-data scenarios.

Abstract

This paper tackles the challenging problem of jointly inferring time-varying network topologies and imputing missing data from partially observed graph signals. We propose a unified non-convex optimization framework to simultaneously recover a sequence of graph Laplacian matrices while reconstructing the unobserved signal entries. Unlike conventional decoupled methods, our integrated approach facilitates a bidirectional flow of information between the graph and signal domains, yielding superior robustness, particularly in high missing-data regimes. To capture realistic network dynamics, we introduce a fused-lasso type regularizer on the sequence of Laplacians. This penalty promotes temporal smoothness by penalizing large successive changes, thereby preventing spurious variations induced by noise while still permitting gradual topological evolution. For solving the joint optimization problem, we develop an efficient Proximal Alternating Direction Method of Multipliers (PADMM) algorithm, which leverages the problem's structure to yield closed-form solutions for both the graph and signal subproblems. This design ensures scalability to large-scale networks and long time horizons. On the theoretical front, despite the inherent non-convexity, we establish a convergence guarantee, proving that the proposed PADMM scheme converges to a stationary point. Furthermore, we derive non-asymptotic statistical guarantees, providing high-probability error bounds for the graph estimator as a function of sample size, signal smoothness, and the intrinsic temporal variability of the graph. Extensive numerical experiments validate the approach, demonstrating that it significantly outperforms state-of-the-art baselines in both convergence speed and the joint accuracy of graph learning and signal recovery.