netsci-labs
graphein
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| netsci-labs | graphein | |
|---|---|---|
| 1 | 2 | |
| 13 | 978 | |
| - | - | |
| 6.1 | 7.8 | |
| 2 months ago | 5 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | MIT License |
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netsci-labs
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My network science / ML on graphs notebooks
A huge inspiration is the Stanford's cs224w course. The notebooks are structured for teaching. https://github.com/zademn/netsci-labs
graphein
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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.
What are some alternatives?
QuickQanava - :link: C++17 network / graph visualization library - Qt6 / QML node editor.
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
egsis - EGSIS: Exploratory Graph-based Semi-supervised Image Segmentation
AllenSDK - code for reading and processing Allen Institute for Brain Science data
awesome-network-analysis - A curated list of awesome network analysis resources.
fastai - The fastai deep learning library
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.
benchmarking-gnns - Repository for benchmarking graph neural networks