graph_nets
typedb-ml
| graph_nets | typedb-ml | |
|---|---|---|
| 2 | 1 | |
| 5,322 | 548 | |
| 0.0% | - | |
| 1.8 | 0.0 | |
| over 1 year ago | 6 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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graph_nets
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[D] Graph neural networks
You can also have a look at these later surveys that give an idea of the different types of GNNs. Also if you prefer Tensorflow you can use the Graph Nets library.
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RL Agent Library to use graph in spaces
I don't know if any RL library includes an already implemented agent that can process graphs. However there are a number of deep learning frameworks that can help with the implementation of graph neural networks, especially Graph Nets (based on Tensorflow) and PyTorch Geometric. You might need to modify an existing RL agent to make use of one of these frameworks. If you are not familiar with GNNs you can look up these surveys. This article may also be of interest to you: it tackles graph-based environments, and the paper's code is available (it has a custom implementation of A2C and uses PyTorch Geometric -- btw it doesn't use Gym's space.graph since this feature is very recent in Gym).
typedb-ml
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Graph theory, graph convolutional networks, knowledge graphs
It's always funny to see people mentioning hypergraphs in relation to knowledge graphs, this is exactly what we do at Grakn Labs (disclaimer: work there) https://grakn.ai
For others: we're also starting to look into ML on knowledge graphs, check out our initial work at https://github.com/graknlabs/kglib :D
What are some alternatives?
pytorch_geometric - Graph Neural Network Library for PyTorch
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
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!
dgl-ke - High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
sr-drl - Implementation of Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks
0xDeCA10B - Sharing Updatable Models (SUM) on Blockchain
Keras - Deep Learning for humans
TrainInvaders - 👾 Jupyter Notebook + Space Invaders!?
GPT2-api - 🤖 (Easily) run your own GPT-2 API. Post writing prompts, get AI-generated responses
ANN-decompiler - "AI" demystified: a decompiler
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
PyNeuraLogic - PyNeuraLogic lets you use Python to create Differentiable Logic Programs