gnn
GNNPapers
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| gnn | GNNPapers | |
|---|---|---|
| 3 | 1 | |
| 1,273 | 15,531 | |
| 3.6% | 1.2% | |
| 9.4 | 0.0 | |
| 3 days ago | 4 months ago | |
| Python | ||
| Apache License 2.0 | - |
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.
gnn
- FLaNK Stack Weekly 19 Feb 2024
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Google Researchers Open-Source the TensorFlow GNN (TF-GNN): A Scalable Python Library for Graph Neural Networks in TensorFlow
Continue reading | Checkout the paper and github link
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TensorFlow Introduces TensorFlow Graph Neural Networks (TF-GNNs)
Github: https://github.com/tensorflow/gnn
GNNPapers
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[D] Masked Node Classification/Representation Learning for Graph Neural Networks
I recently stumbled upon this amazing resource, you might wanna take a look on the papers listed there to check if anything catches your eye. Sorry for not being more helpful!
What are some alternatives?
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
awesome-ml4co - Awesome machine learning for combinatorial optimization papers.
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PDN - The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)
deep-finance - Datasets, papers and books on AI & Finance.
gnn-lspe - Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
awesome-drug-pair-scoring - Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)
diffnet - Graph Neural Network based Social Recommendation Model. SIGIR2019.
Awesome-Image-Inpainting - A curated list of image inpainting and video inpainting papers and resources
ncem - Learning cell communication from spatial graphs of cells
ABigSurvey - A collection of 1000+ survey papers on Natural Language Processing (NLP) and Machine Learning (ML).