# The Dual Graph Shift Operator: Identifying the Support of the Frequency   Domain

**Authors:** Geert Leus, Santiago Segarra, Alejandro Ribeiro, Antonio G. Marques

arXiv: 1705.08987 · 2017-05-26

## TL;DR

This paper introduces the dual graph shift operator to better interpret the support of graph frequency signals, enabling improved analysis, filtering, and understanding of graph-structured data.

## Contribution

It proposes the dual GSO, a novel concept that captures the support of graph frequency signals as a graph, enhancing interpretability and processing in graph signal analysis.

## Key findings

- Dual GSO provides a richer interpretation of graph frequency support.
- Enables development of improved graph filters and filter banks.
- Facilitates generalization of classical signal processing results to graphs.

## Abstract

Contemporary data is often supported by an irregular structure, which can be conveniently captured by a graph. Accounting for this graph support is crucial to analyze the data, leading to an area known as graph signal processing (GSP). The two most important tools in GSP are the graph shift operator (GSO), which is a sparse matrix accounting for the topology of the graph, and the graph Fourier transform (GFT), which maps graph signals into a frequency domain spanned by a number of graph-related Fourier-like basis vectors. This alternative representation of a graph signal is denominated the graph frequency signal. Several attempts have been undertaken in order to interpret the support of this graph frequency signal, but they all resulted in a one-dimensional interpretation. However, if the support of the original signal is captured by a graph, why would the graph frequency signal have a simple one-dimensional support? That is why, for the first time, we propose an irregular support for the graph frequency signal, which we coin the dual graph. The dual GSO leads to a better interpretation of the graph frequency signal and its domain, helps to understand how the different graph frequencies are related and clustered, enables the development of better graph filters and filter banks, and facilitates the generalization of classical SP results to the graph domain.

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/1705.08987/full.md

## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/1705.08987/full.md

## References

15 references — full list in the complete paper: https://tomesphere.com/paper/1705.08987/full.md

---
Source: https://tomesphere.com/paper/1705.08987