67 citations · 103 across the 8 of their papers we have counts for
3 papers · 1 filter
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…
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…