67 citations · 103 across the 8 of their papers we have counts for
8 papers
Inferring the Graph Structure of Images for Graph Neural Networks
Mayur S Gowda, John Shi, Augusto Santos +1
Image datasets such as MNIST are a key benchmark for testing Graph Neural Network (GNN) architectures. The images are traditionally represented as a grid graph with each node repre…
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…
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
Lavender Yao Jiang, John Shi, Mark Cheung +2
Graph neural networks (GNNs) extend convolutional neural networks (CNNs) to graph-based data. A question that arises is how much performance improvement does the underlying graph s…
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…