diff --git a/README.md b/README.md
index 1c4fb36c26ca..908bac6fa4a6 100644
--- a/README.md
+++ b/README.md
@@ -25,9 +25,10 @@ DGL is an easy-to-use, high performance and scalable Python package for deep lea
 
 DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks.
 
-### Models, modules and benchmarks for GNN researchers
+### A versatile tool for GNN researchers and practitioners
 
-The field of graph deep learning is still rapidly evolving and many research ideas emerge by standing on the shoulders of giants. To ease the process, DGL collects a rich set of [example implementations](https://github.com/dmlc/dgl/tree/master/examples) of popular GNN models of a wide range of topics. Researchers can [search](https://www.dgl.ai/) for related models to innovate new ideas from or use them as baselines for experiments. Moreover, DGL provides many state-of-the-art [GNN layers and modules](https://docs.dgl.ai/api/python/nn.html) for users to build new model architectures. DGL is one of the preferred platforms for many standard graph deep learning benchmarks including [OGB](https://ogb.stanford.edu/) and [GNNBenchmarks](https://github.com/graphdeeplearning/benchmarking-gnns).
+The field of graph deep learning is still rapidly evolving and many research ideas emerge by standing on the shoulders of giants. To ease the process, [DGl-Go](https://github.com/dmlc/dgl/tree/master/dglgo) is a command-line interface to get started with training, using and studying state-of-the-art GNNs.
+DGL collects a rich set of [example implementations](https://github.com/dmlc/dgl/tree/master/examples) of popular GNN models of a wide range of topics. Researchers can [search](https://www.dgl.ai/) for related models to innovate new ideas from or use them as baselines for experiments. Moreover, DGL provides many state-of-the-art [GNN layers and modules](https://docs.dgl.ai/api/python/nn.html) for users to build new model architectures. DGL is one of the preferred platforms for many standard graph deep learning benchmarks including [OGB](https://ogb.stanford.edu/) and [GNNBenchmarks](https://github.com/graphdeeplearning/benchmarking-gnns).
 
 ### Easy to learn and use
 
@@ -35,9 +36,7 @@ DGL provides a plenty of learning materials for all kinds of users from ML resea
 
 ### Scalable and efficient
 
-It is convenient to train models using DGL on large-scale graphs across multiple GPUs or multiple machines. DGL extensively optimizes the whole stack to reduce the overhead in communication, memory consumption and synchronization. As a result, DGL can easily scale to billion-sized graphs. See the [system performance note](https://docs.dgl.ai/performance.html) for the comparison with the other tools.
-
-Now DistDGL ParMETIS implementation also provides support for hetero graph by adding back the dropped edges back into the partitioned graph thus handling parMetis hetero graph usecase.
+It is convenient to train models using DGL on large-scale graphs across **multiple GPUs** or **multiple machines**. DGL extensively optimizes the whole stack to reduce the overhead in communication, memory consumption and synchronization. As a result, DGL can easily scale to billion-sized graphs. Get started with the [tutorials](https://docs.dgl.ai/en/tutorials/dist/index.html) and [user guide](https://docs.dgl.ai/en/latest/guide/distributed.html) for distributed training. See the [system performance note](https://docs.dgl.ai/performance.html) for the comparison with other tools.
 
 ## Get Started
 
@@ -47,6 +46,7 @@ For absolute beginners, start with [the Blitz Introduction to DGL](https://docs.
 
 For acquainted users who wish to learn more,
 
+* Experience state-of-the-art GNN models in only two command-lines using [DGL-Go](https://github.com/dmlc/dgl/tree/master/dglgo).
 * Learn DGL by [example implementations](https://www.dgl.ai/) of popular GNN models.
 * Read the [User Guide](https://docs.dgl.ai/guide/index.html) ([中文版链接](https://docs.dgl.ai/guide_cn/index.html)), which explains the concepts and usage of DGL in much more details.
 * Go through the tutorials for advanced features like [stochastic training of GNNs](https://docs.dgl.ai/tutorials/large/index.html), training on [multi-GPU](https://docs.dgl.ai/tutorials/multi/index.html) or [multi-machine](https://docs.dgl.ai/tutorials/dist/index.html).