how_attentive_are_gats
grand-cypher
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| how_attentive_are_gats | grand-cypher | |
|---|---|---|
| 1 | 4 | |
| 275 | 61 | |
| 6.2% | - | |
| 0.0 | 6.0 | |
| about 2 years ago | 2 months ago | |
| Python | Python | |
| - | Apache License 2.0 |
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how_attentive_are_gats
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Graph Attention Networks (GAT) v2 implementation with side-by-side notes
Code for https://arxiv.org/abs/2105.14491 found: https://github.com/tech-srl/how_attentive_are_gats
grand-cypher
What are some alternatives?
GAT - Graph Attention Networks (https://arxiv.org/abs/1710.10903)
dotmotif - A performant, powerful query framework to search for network motifs
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
grand - Your favorite Python graph libraries, scalable and interoperable. Graph databases in memory, and familiar graph APIs for cloud databases.
bottleneck - Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
pytorch-GAT - My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
movies-python-bolt - Neo4j Movies Example application with Flask backend using the neo4j-python-driver