Picture for John Shi

John Shi

GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model

Add code
Mar 04, 2023
Figure 1 for GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model
Figure 2 for GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model
Figure 3 for GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model
Figure 4 for GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model
Viaarxiv icon

The Companion Model -- a Canonical Model in Graph Signal Processing

Add code
Mar 25, 2022
Figure 1 for The Companion Model -- a Canonical Model in Graph Signal Processing
Figure 2 for The Companion Model -- a Canonical Model in Graph Signal Processing
Figure 3 for The Companion Model -- a Canonical Model in Graph Signal Processing
Figure 4 for The Companion Model -- a Canonical Model in Graph Signal Processing
Viaarxiv icon

Graph Signal Processing: A Signal Representation Approach to Convolution and Sampling

Add code
Mar 26, 2021
Figure 1 for Graph Signal Processing: A Signal Representation Approach to Convolution and Sampling
Viaarxiv icon

Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification

Add code
Dec 16, 2020
Figure 1 for Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
Figure 2 for Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
Figure 3 for Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
Figure 4 for Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
Viaarxiv icon

Pooling in Graph Convolutional Neural Networks

Add code
Apr 07, 2020
Figure 1 for Pooling in Graph Convolutional Neural Networks
Figure 2 for Pooling in Graph Convolutional Neural Networks
Figure 3 for Pooling in Graph Convolutional Neural Networks
Viaarxiv icon