- You can find **ERNIESage**, a novel model for modeling text and graph structures, and its introduction [here](./examples/erniesage/).
- PGL for [Open Graph Benchmark](https://github.com/snap-stanford/ogb) examples can be find [here](./ogb_examples/).
- We add newly graph level operators like **GraphPooling** and [**GraphNormalization**](https://arxiv.org/abs/2003.00982) for graph level predictions.
- We relase a PGL-KE toolkit [here](./examples/pgl-ke) including classical knowledge graph embedding t algorithms like TransE, TransR, RotatE.
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Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle).
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle).
<imgsrc="./docs/source/_static/framework_of_pgl.png"alt="The Framework of Paddle Graph Learning (PGL)"width="800">
<imgsrc="./docs/source/_static/framework_of_pgl_en.png"alt="The Framework of Paddle Graph Learning (PGL)"width="800">
The newly released PGL supports heterogeneous graph learning on both walk based paradigm and message-passing based paradigm by providing MetaPath sampling and Message Passing mechanism on heterogeneous graph. Furthermor, The newly released PGL also support distributed graph storage and some distributed training algorithms, such as distributed deep walk and distributed graphsage. Combined with the PaddlePaddle deep learning framework, we are able to support both graph representation learning models and graph neural networks, and thus our framework has a wide range of graph-based applications.
The newly released PGL supports heterogeneous graph learning on both walk based paradigm and message-passing based paradigm by providing MetaPath sampling and Message Passing mechanism on heterogeneous graph. Furthermor, The newly released PGL also support distributed graph storage and some distributed training algorithms, such as distributed deep walk and distributed graphsage. Combined with the PaddlePaddle deep learning framework, we are able to support both graph representation learning models and graph neural networks, and thus our framework has a wide range of graph-based applications.
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## Model Zoo
## Model Zoo
The following are 13 graph learning models that have been implemented in the framework. See the details [here](https://pgl.readthedocs.io/en/latest/introduction.html#highlight-tons-of-models)
The following graph learning models have been implemented in the framework. You can find more [examples](./examples) and the [details](https://pgl.readthedocs.io/en/latest/introduction.html#highlight-tons-of-models)
|Model | feature |
|Model | feature |
|---|---|
|---|---|
| [**ERNIESage**](./examples/erniesage/) | ERNIE SAmple aggreGatE for Text and Graph |
| GCN | Graph Convolutional Neural Networks |
| GCN | Graph Convolutional Neural Networks |
| GAT | Graph Attention Network |
| GAT | Graph Attention Network |
| GraphSage |Large-scale graph convolution network based on neighborhood sampling|
| GraphSage |Large-scale graph convolution network based on neighborhood sampling|
