GSPBOX: A toolbox for signal processing on graphs

Abstract

This document introduces the Graph Signal Processing Toolbox (GSPBox) a framework that can be used to tackle graph related problems with a signal processing approach. It explains the structure and the organization of this software. It also contains a general description of the important modules.

References in corpus (5)

• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• Accelerated filtering on graphs using Lanczos method
• Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames
• Vertex-Frequency Analysis on Graphs
• A Multiscale Pyramid Transform for Graph Signals

Cited by in corpus (48)

• Stationary signal processing on graphs
• Forecasting Time Series with VARMA Recursions on Graphs
• Fast Robust PCA on Graphs
• Advances in Distributed Graph Filtering
• Approximate fast graph Fourier transforms via multi-layer sparse approximations
• Two-Channel Critically-Sampled Graph Filter Banks With Spectral Domain Sampling
• Compressive Spectral Clustering
• Fast Graph Sampling Set Selection Using Gershgorin Disc Alignment
• Large Scale Graph Learning from Smooth Signals
• Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
• Accelerated filtering on graphs using Lanczos method
• Low-complexity Graph Sampling with Noise and Signal Reconstruction via Neumann Series
• Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households
• How to learn a graph from smooth signals
• Splines and Wavelets on Circulant Graphs
• Graph Signal Sampling Under Stochastic Priors
• Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity
• Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift
• A Unified Algorithmic Framework for Distributed Adaptive Signal and Feature Fusion Problems -- Part I: Algorithm Derivation
• Limited-memory polynomial methods for large-scale matrix functions
• Joint Time-Vertex Fractional Fourier Transform
• Sparse Graph Learning Under Laplacian-Related Constraints
• Observing and Tracking Bandlimited Graph Processes
• Preconditioned Gradient Descent Algorithm for Inverse Filtering on Spatially Distributed Networks
• Polynomial graph filter of multiple shifts and distributed implementation of inverse filtering
• Distributionally Robust Graph Learning from Smooth Signals under Moment Uncertainty
• Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction
• Signal Processing on Product Spaces
• Learning Product Graphs Underlying Smooth Graph Signals
• A Markov Variation Approach to Smooth Graph Signal Interpolation
• Random sampling of bandlimited signals on graphs
• Alternating minimization algorithms for graph regularized tensor completion
• Task-Based Graph Signal Compression
• Filter Design for Autoregressive Moving Average Graph Filters
• Robust recovery of bandlimited graph signals via randomized dynamical sampling
• Scalable $M$-Channel Critically Sampled Filter Banks for Graph Signals
• Graph Blind Deconvolution with Sparseness Constraint
• Spline-Like Wavelet Filterbanks with Perfect Reconstruction on Arbitrary Graphs
• Compressive PCA for Low-Rank Matrices on Graphs
• Design of Sampling Set for Bandlimited Graph Signal Estimation
• Gasper: GrAph Signal ProcEssing in R
• Adaptive Graph-based Total Variation for Tomographic Reconstructions
• Online Distributed Learning over Graphs with Multitask Graph-Filter Models
• Active Sampling for Approximately Bandlimited Graph Signals
• Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back
• Graphs for deep learning representations
• Accelerated Spectral Clustering Using Graph Filtering Of Random Signals
• How likely is a random graph shift-enabled?