Sampling and Recovery of Graph Signals

TL;DR

This paper reviews recent methods for sampling and recovering signals on graphs, focusing on perfect recovery conditions, noise mitigation, and adaptive strategies for dynamic signals in complex networks.

Contribution

It provides a comprehensive overview of sampling criteria, recovery algorithms, and strategies for dynamic graph signals, highlighting recent advances in the field.

Findings

Conditions for perfect recovery of bandlimited graph signals.

Sampling strategies that mitigate noise and model mismatches.

Algorithms for adaptive recovery and tracking of dynamic graph signals.

Abstract

The aim of this chapter is to give an overview of the recent advances related to sampling and recovery of signals defined over graphs. First, we illustrate the conditions for perfect recovery of bandlimited graph signals from samples collected over a selected set of vertexes. Then, we describe some sampling design criteria proposed in the literature to mitigate the effect of noise and model mismatching when performing graph signal recovery. Finally, we illustrate algorithms and optimal sampling strategies for adaptive recovery and tracking of dynamic graph signals, where both sampling set and signal values are allowed to vary with time. Numerical simulations carried out over both synthetic and real data illustrate the potential advantages of graph signal processing methods for sampling, interpolation, and tracking of signals observed over irregular domains such as, e.g., technological or biological networks.