how_attentive_are_gats
Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022) (by tech-srl)
GAT
Graph Attention Networks (https://arxiv.org/abs/1710.10903) (by PetarV-)
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| how_attentive_are_gats | GAT | |
|---|---|---|
| 1 | 2 | |
| 275 | 3,045 | |
| 6.2% | - | |
| 0.0 | 0.0 | |
| about 2 years ago | about 2 years ago | |
| Python | Python | |
| - | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
how_attentive_are_gats
Posts with mentions or reviews of how_attentive_are_gats.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-08-06.
-
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
GAT
Posts with mentions or reviews of GAT.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-09.
-
[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
-
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?
When comparing how_attentive_are_gats and GAT you can also consider the following projects:
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
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!
bottleneck - Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
awesome-graph-classification - A collection of important graph embedding, classification and representation learning papers with implementations.
CrabNet - Predict materials properties using only the composition information!