bottleneck
GAT
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| bottleneck | GAT | |
|---|---|---|
| 2 | 2 | |
| 90 | 3,045 | |
| - | - | |
| 0.0 | 0.0 | |
| about 2 years ago | about 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
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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
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
What are some alternatives?
GraphMixerNetworks - Official Implementation of Graph Mixer Networks
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!
grand-cypher - Implementation of the Cypher language for searching NetworkX graphs
awesome-graph-classification - A collection of important graph embedding, classification and representation learning papers with implementations.
osmnx - OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.
how_attentive_are_gats - Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)
code2vec - TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"
CrabNet - Predict materials properties using only the composition information!
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.