Graph Convolutional Neural Networks (GCNN) Collection
• Collection
Publisher
Software Engineering Institute
Abstract
Large, complex datasets (e.g., sensor data, web traffic) require new approaches to graph processing. The SEI applied graph signal processing techniques to create new tools for graph convolutional neural networks (GCNNs), extending deep learning to graph problems.
Collection Items
Graph Convolutional Neural Networks
• Poster
By Oren Wright
This poster describes research to use graph convolutional neural networks to extend deep learning.
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Graph Convolutional Neural Networks
• Presentation
By Oren Wright
This project used graph signal processing formalisms to create new deep learning tools for graph convolutional neural networks (GCNNs).
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Graph Convolutional Neural Networks
• Video
By Oren Wright
Watch SEI researcher Mr. Oren Wright discuss using graph signal processing formalisms to create new deep learning tools for graph convolutional neural networks (GCNNs) to answer the question "how does …
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