Update intro in docs

docs/source/index.rst

@@ -17,7 +17,7 @@ DIG: Dive into Graphs is a turnkey library for graph deep learning research.
 Why DIG?
 ^^^^^^^^
 
-The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, and 3D graphs.
+The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, **DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks**, such as graph generation, self-supervised learning, explainability, and 3D graphs.
 
 If you are working or plan to work on research in graph deep learning, DIG enables you to develop your own methods within our extensible framework, and compare with current baseline methods using common datasets and evaluation metrics without extra efforts.
 
@@ -28,8 +28,9 @@ If you are working or plan to work on research in graph deep learning, DIG enabl
    :maxdepth: 1
    :caption: Get Started
 
-   intro/installation
    intro/introduction
+   intro/installation
+
 
 
 .. toctree::

docs/source/intro/introduction.rst

@@ -1,4 +1,4 @@
-Introduction of DIG: Dive into Graphs
+Introduction
 ======
 
 DIG includes unified implementations of **data interfaces**, **common algorithms**, and **evaluation metrics** for several advanced tasks. Our goal is to enable researchers to easily implement and benchmark algorithms. Currently, we consider the following research directions.
@@ -8,7 +8,16 @@ DIG includes unified implementations of **data interfaces**, **common algorithms
 * **Explainability of Graph Neural Networks**: :obj:`dig.xgraph`
 * **Deep Learning on 3D Graphs**: :obj:`dig.threedgraph`
 
-You can refer to `benchmark implementations <https://github.com/divelab/DIG/tree/dig/benchmarks>`_ as examples to use APIs provided in DIG.
+
+We provide a hands-on tutorial for each direction to help you to get started with DIG: 
+
+* `Tutorial for Graph Generation <https://diveintographs.readthedocs.io/en/latest/tutorials/graphdf.html>`_
+* `Tutorial for Self-supervised Learning on Graphs <https://diveintographs.readthedocs.io/en/latest/tutorials/sslgraph.html>`_
+* `Tutorial for Explainability of Graph Neural Networks <https://diveintographs.readthedocs.io/en/latest/tutorials/subgraphx.html>`_
+* `Tutorial for Deep Learning on 3D Graphs <https://diveintographs.readthedocs.io/en/latest/tutorials/threedgraph.html>`_
+
+
+You can also refer to `benchmark implementations <https://github.com/divelab/DIG/tree/dig/benchmarks>`_ as examples to use APIs provided in DIG.
 
 .. image:: ../../imgs/DIG-overview.png
    :width: 100%