Update Readme.

Author: baoleai

README.md

@@ -1,16 +1,16 @@
 ![GL](docs/images/graph-learn.png)
-[简体中文](README_cn.md) | English
-
-Graph-Learn (formerly AliGraph) is a distributed framework designed for the development and application of large-scale graph neural networks.
-It refines and abstracts a set of programming paradigms suitable for common graph neural network models from the practical problems of large-scale graph training, and has been successfully applied to many scenarios such as search recommendation, network security, knowledge graph, etc. within Alibaba.
-
-Graph-Learn provides Python and C++ interfaces for graph sampling operations, and provides a gremlin-like GSL (Graph Sampling Language) interface. For upper layer graph learning models, Graph-Learn provides a set of paradigms and processes for model development, compatible with TensorFlow and PyTorch, providing data layer, model layer interfaces and rich model examples.
-
 [![pypi](https://img.shields.io/pypi/v/graph-learn.svg)](https://pypi.org/project/graph-learn/)
 [![docs](https://img.shields.io/badge/docs-latest-brightgreen.svg)](https://graph-learn.readthedocs.io/en/latest/)
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+[简体中文](README_cn.md) | English
+
+Graph-Learn (formerly AliGraph) is a distributed framework designed for the development and application of large-scale graph neural networks.
+It abstracts a set of programming paradigms suitable for common graph neural network models from the practical problems of large-scale graph training, and has been successfully applied to many scenarios such as search recommendation, network security, knowledge graph, etc. within Alibaba.
+
+Graph-Learn provides both Python and C++ interfaces for graph sampling operations, and provides a gremlin-like GSL (Graph Sampling Language) interface. For upper layer graph learning models, Graph-Learn provides a set of paradigms and processes for model development. It is compatible with TensorFlow and PyTorch, and provides data layer, model layer interfaces and rich model examples.
+
 
 [**Documentation**](https://graph-learn.readthedocs.io/en/latest/)
 
@@ -23,16 +23,20 @@ pip install graph-learn
 
 2. [Build from source](docs/en/install.md)
 
-3. Use Docker
+3. [Use Docker](docs/en/install.md)
 
-4. [k8s](docs/en/algo/tf/k8s.md)
 
-## example
+## Getting Started
+GraphSAGE example
 ```
 cd examples/tf/ego_sage/
 python train_unsupervised.py
 ```
 
+[Distributed training example](docs/en/algo/tf/k8s)
+
+
+
 ## Citation
 
 Please cite the following paper in your publications if **GL** helps your research.

README_cn.md

@@ -1,16 +1,16 @@
 ![GL](docs/images/graph-learn.png)
+[![pypi](https://img.shields.io/pypi/v/graph-learn.svg)](https://pypi.org/project/graph-learn/)
+[![docs](https://img.shields.io/badge/docs-latest-brightgreen.svg)](https://graph-learn.readthedocs.io/zh_CN/latest/)
+[![graph-learn CI](https://github.com/alibaba/graph-learn/workflows/graph-learn%20CI/badge.svg)](https://github.com/alibaba/graph-learn/actions)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/alibaba/graph-learn/blob/master/LICENSE)
+
 简体中文 | [English](README.md)
 
 Graph-Learn(原AliGraph) 是面向大规模图神经网络的研发和应用而设计的一款分布式框架。
 它从大规模图训练的实际问题出发，提炼和抽象了一套适合于常见图神经网络模型的编程范式， 并已经成功应用在阿里巴巴内部的诸如搜索推荐、网络安全、知识图谱等众多场景。
 
 Graph-Learn提供了图采样操作的Python和C++接口，并且提供了一个类似gremlin的GSL(Graph Sampling Language)接口。对于上层图学习模型，Graph-Learn提供了一套模型开发的范式和流程，兼容TensorFlow和PyTorch，提供了数据层，模型层接口和丰富的模型示例。
 
-[![pypi](https://img.shields.io/pypi/v/graph-learn.svg)](https://pypi.org/project/graph-learn/)
-[![docs](https://img.shields.io/badge/docs-latest-brightgreen.svg)](https://graph-learn.readthedocs.io/zh_CN/latest/)
-[![graph-learn CI](https://github.com/alibaba/graph-learn/workflows/graph-learn%20CI/badge.svg)](https://github.com/alibaba/graph-learn/actions)
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/alibaba/graph-learn/blob/master/LICENSE)
-
 [**用户文档**](https://graph-learn.readthedocs.io/zh_CN/latest/)
 
 ## 安装部署
@@ -22,15 +22,15 @@ pip install graph-learn
 
 2. [从源码编译](docs/zh_CN/install.md)
 
-3. 使用Docker
-
-4. [k8s](docs/zh_CN/algo/tf/k8s.md)
+3. [使用Docker](docs/zh_CN/install.md)
 
-## 运行示例
+## 开始使用
+GraphSAGE示例
 ```
 cd examples/tf/ego_sage/
 python train_unsupervised.py
 ```
+[分布式训练示例](docs/zh_CN/algo/tf/k8s)
 
 ## 论文