DeepAI AI Chat
Log In Sign Up

Modelling Graph Errors: Towards Robust Graph Signal Processing

03/20/2019
by   Jari Miettinen, et al.
aalto
0

The first step for any graph signal processing (GSP) procedure is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix. Indeed, the adjacency matrix is typically not known a priori and has to be learned. However, it is learned with errors. A little, if any, attention has been paid to modeling such errors in the adjacency matrix, and studying their effects on GSP methods. However, modeling errors in adjacency matrix will enable both to study the graph error effects in GSP and to develop robust GSP algorithms. In this paper, we therefore introduce practically justifiable graph error models. We also study, both analytically and in terms of simulations, the graph error effect on the performance of GSP methods based on the examples of more traditional different types of filtering of graph signals and less known independent component analysis (ICA) of graph signals (graph decorrelation).

READ FULL TEXT

page 1

page 2

page 3

page 4

03/15/2022

Graph Neural Network Sensitivity Under Probabilistic Error Model

Graph convolutional networks (GCNs) can successfully learn the graph sig...
03/06/2023

Frames for signal processing on Cayley graphs

The spectral decomposition of graph adjacency matrices is an essential i...
08/21/2020

Graph learning under spectral sparsity constraints

Graph inference plays an essential role in machine learning, pattern rec...
09/25/2018

Graph filtering for data reduction and reconstruction

A novel approach is put forth that utilizes data similarity, quantified ...
03/10/2020

Methods of Adaptive Signal Processing on Graphs Using Vertex-Time Autoregressive Models

The concept of a random process has been recently extended to graph sign...
01/20/2023

Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks

The use of complex networks as a modern approach to understanding the wo...
11/25/2017

On the Inverse of Forward Adjacency Matrix

During routine state space circuit analysis of an arbitrarily connected ...