未验证 提交 bf3b794f 编写于 作者: H Huang Zhengjie 提交者: GitHub

Merge pull request #9 from Yelrose/master

Fixed Docs
......@@ -63,8 +63,7 @@ If we use complex user-defined aggregation like [GraphSAGE-LSTM](https://cs.stan
PGL requires:
* paddle >= 1.5
* networkx
* paddle >= 1.6
* cython
......@@ -73,7 +72,7 @@ PGL supports both Python 2 & 3
## Installation
The current version of PGL is 0.1.0.beta. You can simply install it via pip.
The current version of PGL is 1.0.0. You can simply install it via pip.
```sh
pip install pgl
......
......@@ -58,8 +58,7 @@ Paddle Graph Learning (PGL)是一个基于[PaddlePaddle](https://github.com/Padd
PGL依赖于:
* paddle >= 1.5
* networkx
* paddle >= 1.6
* cython
......@@ -68,7 +67,7 @@ PGL支持Python 2和3。
## 安装
当前,PGL的版本是0.1.0.beta。你可以简单的用pip进行安装。
当前,PGL的版本是1.0.0。你可以简单的用pip进行安装。
```sh
pip install pgl
......@@ -81,4 +80,3 @@ PGL由百度的NLP以及Paddle团队共同开发以及维护。
## License
PGL uses Apache License 2.0.
......@@ -3,5 +3,6 @@ mistune
sphinx_rtd_theme
numpy >= 1.16.4
cython >= 0.25.2
networkx
paddlepaddle
pgl
......@@ -40,7 +40,7 @@ copyright = '2019, PaddlePaddle'
author = 'PaddlePaddle'
# The full version, including alpha/beta/rc tags
release = '0.1.0.beta'
release = '1.0.0'
# -- General configuration ---------------------------------------------------
......
# PGL Examples for GCN
# PGL Examples for DGI
[Deep Graph Infomax \(DGI\)](https://arxiv.org/abs/1809.10341) is a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures.
......
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