[Doc] Tutorials re-organization (#2683)

* reorg

* change titles

* rm some stale API doc; minor fix

* fix docs

* add warning

* rm new-tutorial run in ci

* lint

tutorials/blitz/.gitignore

+*.dgl
+*.csv

tutorials/blitz/README.txt

+A Blitz Introduction to DGL
+===========================

tutorials/large/.gitignore

+dataset
+model.pt

new-tutorial/large/L4_message_passing.py → tutorials/large/L4_message_passing.py

@@ -224,7 +224,7 @@ with tqdm.tqdm(train_dataloader) as tq:
 #
 # Here is a step-by-step tutorial for writing a GNN module for both
 # :doc:`full-graph training <../blitz/1_introduction>` *and* :doc:`stochastic
-# training <L1_node_classification>`.
+# training <L1_large_node_classification>`.
 #
 # Say you start with a GNN module that works for full-graph training only:
 #

tutorials/large/README.txt

+Stochastic Training of GNNs
+===========================

tutorials/models/1_gnn/1_gcn.py

@@ -7,16 +7,19 @@ Graph Convolutional Network
 **Author:** `Qi Huang <https://github.com/HQ01>`_, `Minjie Wang  <https://jermainewang.github.io/>`_,
 Yu Gai, Quan Gan, Zheng Zhang
 
+.. warning::
+
+    The tutorial aims at gaining insights into the paper, with code as a mean
+    of explanation. The implementation thus is NOT optimized for running
+    efficiency. For recommended implementation, please refer to the `official
+    examples <https://github.com/dmlc/dgl/tree/master/examples>`_.
+
 This is a gentle introduction of using DGL to implement Graph Convolutional
 Networks (Kipf & Welling et al., `Semi-Supervised Classification with Graph
 Convolutional Networks <https://arxiv.org/pdf/1609.02907.pdf>`_). We explain
-what is under the hood of the :class:`~dgl.nn.pytorch.GraphConv` module.
+what is under the hood of the :class:`~dgl.nn.GraphConv` module.
 The reader is expected to learn how to define a new GNN layer using DGL's
 message passing APIs.
-
-We build upon the :doc:`earlier tutorial <../../basics/3_pagerank>` on DGLGraph
-and demonstrate how DGL combines graph with deep neural network and learn
-structural representations.
 """
 
 ###############################################################################
@@ -179,8 +182,7 @@ for epoch in range(50):
 # The equation can be efficiently implemented using sparse matrix
 # multiplication kernels (such as Kipf's
 # `pygcn <https://github.com/tkipf/pygcn>`_ code). The above DGL implementation
-# in fact has already used this trick due to the use of builtin functions. To
-# understand what is under the hood, please read our tutorial on :doc:`PageRank <../../basics/3_pagerank>`.
+# in fact has already used this trick due to the use of builtin functions.
 #
 # Note that the tutorial code implements a simplified version of GCN where we
 # replace :math:`\tilde{D}^{-\frac{1}{2}}\tilde{A}\tilde{D}^{-\frac{1}{2}}` with

tutorials/models/1_gnn/4_rgcn.py

 """
 .. _model-rgcn:
 
-Relational graph convolutional network
+Relational Graph Convolutional Network
 ================================================
 
 **Author:** Lingfan Yu, Mufei Li, Zheng Zhang
 
+.. warning::
+
+    The tutorial aims at gaining insights into the paper, with code as a mean
+    of explanation. The implementation thus is NOT optimized for running
+    efficiency. For recommended implementation, please refer to the `official
+    examples <https://github.com/dmlc/dgl/tree/master/examples>`_.
+
 In this tutorial, you learn how to implement a relational graph convolutional
 network (R-GCN). This type of network is one effort to generalize GCN 
 to handle different relationships between entities in a knowledge base. To 

tutorials/models/1_gnn/6_line_graph.py

 """
 .. _model-line-graph:
 
-Line graph neural network
+Line Graph Neural Network
 =========================
 
 **Author**: `Qi Huang <https://github.com/HQ01>`_, Yu Gai,
 `Minjie Wang <https://jermainewang.github.io/>`_, Zheng Zhang
+
+.. warning::
+
+    The tutorial aims at gaining insights into the paper, with code as a mean
+    of explanation. The implementation thus is NOT optimized for running
+    efficiency. For recommended implementation, please refer to the `official
+    examples <https://github.com/dmlc/dgl/tree/master/examples>`_.
+
 """
 
 ###########################################################################################

tutorials/models/1_gnn/9_gat.py

 """
 .. _model-gat:
 
-Graph attention network
-==================================
+Understand Graph Attention Network
+=======================================
 
 **Authors:** `Hao Zhang <https://github.com/sufeidechabei/>`_, `Mufei Li
 <https://github.com/mufeili>`_, `Minjie Wang
 <https://jermainewang.github.io/>`_  `Zheng Zhang
 <https://shanghai.nyu.edu/academics/faculty/directory/zheng-zhang>`_
 
+.. warning::
+
+    The tutorial aims at gaining insights into the paper, with code as a mean
+    of explanation. The implementation thus is NOT optimized for running
+    efficiency. For recommended implementation, please refer to the `official
+    examples <https://github.com/dmlc/dgl/tree/master/examples>`_.
+
 In this tutorial, you learn about a graph attention network (GAT) and how it can be 
 implemented in PyTorch. You can also learn to visualize and understand what the attention 
 mechanism has learned.

tutorials/models/1_gnn/README.txt

 .. _tutorials1-index:
 
 Graph neural networks and its variants
-====================================
+--------------------------------------------
 
 * **Graph convolutional network (GCN)** `[research paper] <https://arxiv.org/abs/1609.02907>`__ `[tutorial]
   <1_gnn/1_gcn.html>`__ `[Pytorch code]
   <https://github.com/dmlc/dgl/blob/master/examples/pytorch/gcn>`__
   `[MXNet code]
   <https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn>`__:
-  This is the most basic GCN. The tutorial covers the basic uses of DGL APIs.
 
 * **Graph attention network (GAT)** `[research paper] <https://arxiv.org/abs/1710.10903>`__ `[tutorial]
   <1_gnn/9_gat.html>`__ `[Pytorch code]