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GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model
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 -- a Canonical Model in Graph Signal Processing
This paper introduces a $\textit{canonical}$ graph signal model defined by a $\textit{canonical}$ graph and a $\textit{canonical}$ shift, the $\textit{companion}$ graph and the $\textit{companion}$ shift.
Graph Signal Processing: Dualizing GSP Sampling in the Vertex and Spectral Domains
This paper shows that in fact one can develop a unified graph signal sampling theory with analogous interpretations in both domains just like sampling in traditional DSP.
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
Graph neural networks (GNNs) extend convolutional neural networks (CNNs) to graph-based data.
Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology
Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains.
Pooling in Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems.
