Learning Laplacian Forms for Graph Signal Processing via the Deformed Laplacian

TL;DR

This paper introduces a parametric deformed Laplacian for graph signal processing, enabling flexible topological operators and improved signal representation by jointly learning the Laplacian and signals from data.

Contribution

The paper proposes a novel parametric Laplacian called the deformed Laplacian, unifying various Laplacian variants and enhancing graph signal processing through joint learning from data.

Findings

The deformed Laplacian encompasses several existing Laplacian variants as special cases.

Joint learning of the deformed Laplacian and signals improves reconstruction accuracy.

Numerical experiments demonstrate superior performance on synthetic and real-world datasets.

Abstract

Learning the graph Laplacian from observed data is one of the most investigated and fundamental tasks in Graph Signal Processing (GSP). Different variants of the Laplacian, such as the combinatorial, signless or signed Laplacians have been considered depending on the type of features to be extracted from the data. The main contribution of this paper is the introduction of a parametric Laplacian, called the deformed Laplacian, defined as a quadratic matrix polynomial that provides a parametric dictionary for graph signal processing. The deformed Laplacian can be interpreted as the generator of a parametric linear reaction-diffusion dynamics on graphs, capturing the interplay between diffusive coupling and nodal reaction effects. It is a parametric polynomial matrix that enables the design of novel topological operators tailored to both the underlying graph structure and the observed signals. Interestingly, we show that several Laplacian variants proposed in the literature arise as special cases of the deformed Laplacian. We then develop a method to jointly learn the deformed Laplacian and the graph signals from data, showing how its use improves signal representation across a broad class of graphs compared to standard Laplacian forms. Through extensive numerical experiments on both synthetic and real-world datasets, including financial and communication networks, we assess the benefits of the proposed method in terms of graph signal reconstruction error and sparsity of the representation.