Introduction to Graph Neural Networks for Machine Learning Engineers
Overview & Synopsis
Graph Neural Networks (GNN) are deep neural networks designed for graphs with attributes attached to nodes or edges. This survey introduces graph neural networks through the encoder-decoder framework and provides examples of decoders for a range of graph analytic tasks.
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Document Information
| Document Title | Introduction to Graph Neural Networks for Machine Learning Engineers |
|---|---|
| Author / Organization | James H. Tanis, Chris Giannella, Adrian V. Mariano, Daoud Meerzaman |
| Subject & Discipline | Neural Networks, Deep Learning, Machine Learning, Graph Theory |
| Categories | Neural Networks |
| Publication Year | 2026 |
| License / Rights | Creative Commons License (CC) |
| Resource Verification | ✓ Verified Direct PDF Available |
| Original Host Website |
FreeComputerBooks.com
(freecomputerbooks.com)
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| File Format | application/pdf (Portable Document Format) |
| Language | ENGLISH |
| Catalog ID | DOC-8812 |
| Original Source Link | Visit Source Page on freecomputerbooks.com » |