172 citations · 516 across the 28 of their papers we have counts for
9 papers · 1 filter
Graph Signal Processing: History, Development, Impact, and Outlook
Geert Leus, Antonio G. Marques, José M. F. Moura +2
Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are ve…
GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model
John Shi, Jose M. F. Moura
The paper presents the graph signal processing (GSP) companion model that naturally replicates the basic tenets of classical signal processing (DSP) for GSP. The companion model sh…
The Companion Model -- a Canonical Model in Graph Signal Processing
John Shi, Jose M. F. Moura
This paper introduces a graph signal model defined by a graph and a shift, the graph and the $\t…
Graph Signal Processing: Dualizing GSP Sampling in the Vertex and Spectral Domains
John Shi, Jose M. F. Moura
Vertex based and spectral based GSP sampling has been studied recently. The literature recognizes that methods in one domain do not have a counterpart in the other domain. This pap…
Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology
Mark Cheung, John Shi, Oren Wright +3
Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains. But conventional deep learnin…
Pooling in Graph Convolutional Neural Networks
Mark Cheung, John Shi, Lavender Yao Jiang +2
Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems. We empirically evaluate several pooling methods…
Graph Fourier Transform: A Stable Approximation
João Domingos, José M. F. Moura
In Graph Signal Processing (GSP), data dependencies are represented by a graph whose nodes label the data and the edges capture dependencies among nodes. The graph is represented b…
Graph Signal Processing: Modulation, Convolution, and Sampling
John Shi, Jose M. F. Moura
To analyze data supported by arbitrary graphs G, DSP has been extended to Graph Signal Processing (GSP) by redefining traditional DSP concepts like shift, filtering, and Fourier tr…
Graph Signal Processing: Overview, Challenges and Applications
Antonio Ortega, Pascal Frossard, Jelena Kovačević +2
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…