Graph Signal Processing: Filter Design and Spectral Statistics

TL;DR

This paper explores graph signal processing, focusing on filter design and spectral statistics, especially for random graphs, using deterministic spectral approximations to optimize consensus acceleration filters.

Contribution

It introduces deterministic spectral approximation methods for designing graph filters on random graphs, enhancing convergence in distributed consensus.

Findings

Deterministic spectral approximations effectively model random graph spectra.

Optimized filters improve convergence speed in consensus algorithms.

Simulation results validate the proposed spectral-based filter design approach.

Abstract

Graph signal processing analyzes signals supported on the nodes of a graph by defining the shift operator in terms of a matrix, such as the graph adjacency matrix or Laplacian matrix, related to the structure of the graph. With respect to the graph shift operator, polynomial functions of the shift matrix perform filtering. An application considered in this paper, convergence acceleration filters for distributed average consensus may be viewed as lowpass graph filters periodically applied to the states. Design of graph filters depends on the shift matrix eigendecomposition. Consequently, random graphs present a challenge as this information is often difficult to obtain. Nevertheless, the asymptotic behavior of the shift matrix empirical spectral distribution provides a substitute for suitable random matrix models. This paper employs deterministic approximations for empirical spectral statistics from other works to propose optimization criteria for consensus acceleration filters, evaluating the results through simulation.