[BUGFIX] fix the problems in the sampling tutorial. (#523)

* add index.

* update.

* update tutorial.

docs/source/index.rst

@@ -158,6 +158,8 @@ The model tutorials are categorized based on the way they utilize DGL APIs.
 * :ref:`Generative models <tutorials3-index>`: Learn how to deal with **dynamically-changing graphs**.
 * :ref:`Old (new) wines in new bottle <tutorials4-index>`: Learn how to combine DGL with tensor-based
   DGL framework in a flexible way. Explore new perspective on traditional models by graphs.
+* :ref:`Training on giant graphs <tutorials5-index>`: Learn how to train graph neural networks
+  on giant graphs.
 
 Or go through all of them :doc:`here <tutorials/models/index>`.
 

tutorials/models/5_giant_graph/sampling_mx.py → tutorials/models/5_giant_graph/1_sampling_mx.py

@@ -389,8 +389,8 @@ if have_large_memory:
 # computation flow underlying a DAG can be executed in one sweep, by
 # calling ``prop_flows``.
 #
-# ``prop_flows`` accepts a list of UDFs. The code defines node update UDFs
-# for each layer.
+# ``prop_flows`` accepts a list of UDFs. The code below defines node update UDFs
+# for each layer and computes a simplified version of GCN with neighbor sampling.
 #
 
 apply_node_funcs = [

tutorials/models/5_giant_graph/README.txt

@@ -5,7 +5,7 @@ Training on giant graphs
 =============================
 
 * **Sampling** `[paper] <https://arxiv.org/abs/1710.10568>`__ `[tutorial]
-  <5_giant_graph/1_sampling.html>`__ `[MXNet code]
+  <5_giant_graph/1_sampling_mx.html>`__ `[MXNet code]
   <https://github.com/dmlc/dgl/tree/master/examples/mxnet/sampling>`__ `[Pytorch code]
   <https://github.com/dmlc/dgl/tree/master/examples/pytorch/sampling>`__:
   we can perform neighbor sampling and control-variate sampling to train