GAT
bottleneck
| GAT | bottleneck | |
|---|---|---|
| 2 | 2 | |
| 3,045 | 90 | |
| - | - | |
| 0.0 | 0.0 | |
| about 2 years ago | about 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
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GAT
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[D] Dr. Petar Veličković (Deepmind) - Categories, Graphs, Reasoning and Graph Expander Propagation
Found relevant code at https://github.com/PetarV-/GAT + all code implementations here
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Graph Attention Networks (GAT) v2 implementation with side-by-side notes
Code for https://arxiv.org/abs/1710.10903 found: https://github.com/PetarV-/GAT
bottleneck
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[D] Dr. Petar Veličković (Deepmind) - Categories, Graphs, Reasoning and Graph Expander Propagation
Found relevant code at https://github.com/tech-srl/bottleneck + all code implementations here
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[N] New Transport Planning Dataset for Deep Graph Neural Networks
Code for https://arxiv.org/abs/2006.05205 found: https://github.com/tech-srl/bottleneck
What are some alternatives?
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!
GraphMixerNetworks - Official Implementation of Graph Mixer Networks
awesome-graph-classification - A collection of important graph embedding, classification and representation learning papers with implementations.
grand-cypher - Implementation of the Cypher language for searching NetworkX graphs
how_attentive_are_gats - Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)
osmnx - OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.
CrabNet - Predict materials properties using only the composition information!
code2vec - TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"
TransportPlanningDataset - A graph based strategic transport planning dataset, aimed at creating the next generation of deep graph neural networks for transfer learning. Based on simulation results of the Four Step Model in PTV Visum.