graphein
Protein Graph Library (by a-r-j)
benchmarking-gnns
Repository for benchmarking graph neural networks (by graphdeeplearning)
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| graphein | benchmarking-gnns | |
|---|---|---|
| 2 | 1 | |
| 978 | 2,421 | |
| - | 1.7% | |
| 7.8 | 0.0 | |
| 6 days ago | 10 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | 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.
graphein
Posts with mentions or reviews of graphein.
We have used some of these posts to build our list of alternatives
and similar projects.
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Meet Graphein: a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks
Github: https://github.com/a-r-j/graphein
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[Discussion] which NN architecture is best suitable for analysing the structural data of biomolecules
As alluded to by u/WorldWar1Nerd, it depends on the format and structure of your data. However, based on what you have already said about your dataset, a graph neural network (GNN) may be a suitable choice, depending on the task. I recommend looking into a wonderful ML library for proteins called Graphein (https://github.com/a-r-j/graphein) to get started, however, do not be afraid if you find that you need to customize these methods to your specific problem.
benchmarking-gnns
Posts with mentions or reviews of benchmarking-gnns.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-27.
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[D] Laplacian positional encodings
Code for https://arxiv.org/abs/2003.00982 found: https://github.com/graphdeeplearning/benchmarking-gnns
What are some alternatives?
When comparing graphein and benchmarking-gnns you can also consider the following projects:
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
AllenSDK - code for reading and processing Allen Institute for Brain Science data
fastai - The fastai deep learning library
netsci-labs - (In progress) Network science laboratories. Covers graph theory, random graphs and ML on graphs
ktrain - ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
geometric-gnn-dojo - Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.