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Showing 1-25 of 10,270 resultsfor "Index Terms":Graph signal processing
"Index Terms":Graph signal processing
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Porting Signal Processing from Undirected to Directed Graphs: Case Study Signal Denoising with Unrolling Networks
2022 30th European Signal Processing Conference (EUSIPCO)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Directionality is an essential feature of many real-world networks, but problematic in graph signal processing (GSP) because there is no obvious choice of Fourier basis. In this work we investigate how to port GSP methods from undirected to directed graphs using recent work on graph signal denoising using trainable networks as a case study. We consider five notions of directed Fourier bases from t...Show More
Graph Signal Processing: Overview, Challenges, and Applications
Antonio Ortega;Pascal Frossard;Jelena Kovačević;José M. F. Moura;Pierre Vandergheynst
Proceedings of the IEEE
Cited by: Papers (1204)
Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then su...Show More
On Local Distributions in Graph Signal Processing
T. Mitchell Roddenberry;Fernando Gama;Richard G. Baraniuk;Santiago Segarra
Graph filtering is the cornerstone operation in graph signal processing (GSP). Thus, understanding it is key in developing potent GSP methods. Graph filters are local and distributed linear operations, whose output depends only on the local neighborhood of each node. Moreover, a graph filter's output can be computed separately at each node by carrying out repeated exchanges with immediate neighbor...Show More
Time-Varying Signals Recovery Via Graph Neural Networks
Jhon A. Castro-Correa;Jhony H. Giraldo;Anindya Mondal;Mohsen Badiey;Thierry Bouwmans;Fragkiskos D. Malliaros
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
The recovery of time-varying graph signals is a fundamental problem with numerous applications in sensor networks and forecasting in time series. Effectively capturing the spatiotemporal information in these signals is essential for the downstream tasks. Previous studies have used the smoothness of the temporal differences of such graph signals as an initial assumption. Nevertheless, this smoothne...Show More
Graph topology recovery for regular and irregular graphs
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
In this paper, we study the recovery of the graph topology or structure. We first extend our previous work on graph signal recovery to present a joint graph signal and structure recovery framework. By doing this, we allow the algorithm to learn a graph structure from noisy and incomplete graph signals and recover the graph signals at the same time. In this paper, we particularly focus on the speci...Show More
ANALYSIS VS SYNTHESIS - AN INVESTIGATION OF (CO)SPARSE SIGNAL MODELS ON GRAPHS
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
In this work, we present a theoretical study of signals with sparse representations in the vertex domain of a graph, which is primarily motivated by the discrepancy arising from respectively adopting a synthesis and analysis view of the graph Laplacian matrix. Sparsity on graphs and, in particular, the characterization of the subspaces of signals which are sparse with respect to the connectivity o...Show More
Oversampled bipartite graphs with controlled redundancy
2015 23rd European Signal Processing Conference (EUSIPCO)
Year: 2015 | Conference Paper |
This paper extends our previous work on graph oversampling for graph signal processing. In the graph oversampling method, nodes are duplicated and edges are appended to construct oversampled graph Laplacian matrix. It can convert an arbitrary K-colorable graph into one bipartite graph which includes all edges of the original graph. Since it uses a coloring-based algorithm, performance of graph sig...Show More
Optimal Fractional Fourier Filtering for Graph Signals
Graph signal processing has recently received considerable attention. Several concepts, tools, and applications in signal processing such as filtering, transforming, and sampling have been extended to graph signal processing. One such extension is the optimal filtering problem. The minimum mean-squared error estimate of an original graph signal can be obtained from its distorted and noisy version....Show More
Correlation-Based Graph Smoothness Measures In Graph Signal Processing
2023 31st European Signal Processing Conference (EUSIPCO)
Year: 2023 | Conference Paper |
Graph smoothness is an important prior used for designing sampling strategies for graph signals as well as for regularizing the problem of graph learning. Additionally, smoothness is an appropriate assumption for graph signal processing (GSP) tasks such as filtering or signal recovery from samples. The most popular measure of smoothness is the quadratic form of the Laplacian, which naturally follo...Show More
Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Graph signal processing on directed graphs poses theoretical challenges since an eigendecomposition of filters is in general not available. Instead, Fourier analysis requires a Jordan decomposition and the frequency response is given by the Jordan normal form, whose computation is numerically unstable for large sizes. In this paper, we propose to replace a given adjacency shift A by a diagonalizab...Show More
Graph Fourier transform based on directed Laplacian
2016 International Conference on Signal Processing and Communications (SPCOM)
Year: 2016 | Conference Paper |
Cited by: Papers (27)
In this paper, we redefine the graph Fourier transform (GFT) under the DSPG framework. We consider the Jordan eigenvectors of the directed Laplacian matrix as graph harmonics and the corresponding eigenvalues as the graph frequencies. For this purpose, we propose a shift operator based on the directed Laplacian of a graph. Based on our shift operator, we then define total variation of graph signal...Show More
Spectrum-blind signal recovery on graphs
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Year: 2015 | Conference Paper |
Cited by: Papers (13)
We consider the problem of recovering a graph signal, sparse in the graph spectral domain from a few number of samples. In contrast to most previous work on the sampling of graph signals, the setting is “spectrum-blind” where we are unaware of the graph d support of the signal. We propose a class of spectrum-blind graph signals and study two recovery strategies based on random and experimentally d...Show More
Graph Signal Denoising Via Unrolling Networks
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
We propose an interpretable graph neural network framework to denoise single or multiple noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to the graph domain and provide an interpretation of the architecture design from a signal processing perspective. We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the fee...Show More
Density Aware Blue-Noise Sampling on Graphs
2022 30th European Signal Processing Conference (EUSIPCO)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Efficient sampling of graph signals is essential to graph signal processing. Recently, blue-noise was introduced as a sampling method that maximizes the separation between sampling nodes leading to high-frequency dominance patterns, and thus, to high-quality patterns. Despite the simple inter-pretation of the method, blue-noise sampling is restricted to approximately regular graphs. This study pre...Show More
Discrete signal processing on graphs: Graph fourier transform
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2013 | Conference Paper |
We propose a novel discrete signal processing framework for the representation and analysis of datasets with complex structure. Such datasets arise in many social, economic, biological, and physical networks. Our framework extends traditional discrete signal processing theory to structured datasets by viewing them as signals represented by graphs, so that signal coefficients are indexed by graph n...Show More
Gegenbauer Graph Neural Networks for Time-Varying Signal Reconstruction
Jhon A. Castro-Correa;Jhony H. Giraldo;Mohsen Badiey;Fragkiskos D. Malliaros
Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting. Accurately capturing the spatio-temporal information inherent in these signals is crucial for effectively addressing these tasks. However, existing app...Show More
The Signal Feature Extraction of Graph Fourier Transform on the Constructed Graph
2021 6th International Conference on Communication, Image and Signal Processing (CCISP)
Year: 2021 | Conference Paper |
Motivated by graph Fourier transform (GFT), we propose a novel signal processing method for feature extraction. Mapping the original signal into a transformed space by looking for a suitable basis, thereby observing the new feature of the signal in the transform domain, has always been the research content of signal processing. In a finite-dimensional transformation space, the original signal can ...Show More
A survey of signal processing based graph learning techniques
B. Subbareddy;P. Charantej Reddy;Aditya Siripuram;Jingxin Zhang
2019 1st International Conference on Industrial Artificial Intelligence (IAI)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Recent advances in technology have led to easy data acquisition mechanisms in many fields, leading to massive datasets. It is often of interest to understand the inherent structure and learn the best representation of the given dataset. Graphs are a powerful way to model interrelationships between data features - well constructed meaningful graphs help in representing and processing the data effec...Show More
Learning Graph ARMA Processes From Time-Vertex Spectra
Eylem Tuğçe Güneyi;Berkay Yaldız;Abdullah Canbolat;Elif Vural
The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation patterns of the process across different graph nodes and time instants. In this study, we propose an algorithm for computing graph autoregressive moving average (graph ARMA) processes based on learning the joint time-verte...Show More
A Graph Signal Denoising Method Using Total Variation Regularization and Fractional Graph Fourier Transform
2024 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)
Year: 2024 | Conference Paper |
In the research area of graph signal processing (GSP), total variation (TV) regularization has been effectively utilized to develop a graph signal denoising method in the vertex domain. This paper aims to enhance the performance of the traditional TV-based denoising method by first providing the spectral-domain interpretation of the total variation regularization method. Then, the graph Fourier tr...Show More
Graph Convolutional Neural Networks In The Companion Model
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Graph Convolutional Neural Networks (graph CNNs) adapt the traditional CNN architecture for use on graphs, replacing convolution layers with graph convolution layers. Although similar in architecture, graph CNNs are used for geometric deep learning whereas conventional CNNs are used for deep learning on grid-based data, such as audio or images, with seemingly no direct relationship between the two...Show More
Graph Fourier Transform Centrality for Taipei Metro System
2020 International Symposium on Computer, Consumer and Control (IS3C)
Year: 2020 | Conference Paper |
Cited by: Papers (8)
In this paper, the graph Fourier transform (GFT) centrality is used as node importance measure to identify central stations of the Taipei metro system. First, the computation algorithm of the GFT centrality is presented. Then, GFT centrality is employed to analyze Taipei metro system and determine which stations are more central. This information is useful to analyze the vulnerability of the metro...Show More
Multi-Resolution Spectral Graph Matching
2019 IEEE International Conference on Image Processing (ICIP)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
In this paper we study the problem of inexact weighted graph matching, where the goal is to find the correspondence between the vertices of two similar graphs. We propose a novel multi-resolution approach that improves the performance of single resolution graph matching which can be jointly combined with state-of-the art graph matching algorithms. Spectral graph matching determines the best corres...Show More
An Efficient Algorithm for Learning Locally Stationary Graph Processes
2024 32nd European Signal Processing Conference (EUSIPCO)
Year: 2024 | Conference Paper |
The modeling of graph signals as stochastic processes has led to quite accurate inference algorithms in the recent years. While most methods assume a globally stationary process model that is valid on the whole graph, in many practical settings the statistics of the process may vary locally, providing motivation for locally stationary graph process (LSGP) models. In this work, we address the probl...Show More
Graph Learning Under Spectral Sparsity Constraints
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
Graph inference plays an essential role in machine learning, pattern recognition, and classification. Signal processing based approaches in literature generally assume some variational property of the observed data on the graph. We make a case for inferring graphs on which the observed data has high variation. We propose a new signal processing based inference model and a new learning criterion th...Show More
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