[Doc] fix the giant graph tutorial (#591)

* fix.

* fix.

tutorials/models/5_giant_graph/2_giant.py

@@ -50,7 +50,7 @@ Large-Scale Training of Graph Neural Networks
 #
 # The graph store has two parts: the server and the client. We need to run
 # the graph store server as a daemon before training. We provide a script
-# ```run_store_server.py`` <https://github.com/zheng-da/dgl-1/blob/sampling-example/examples/mxnet/sampling/run_store_server.py>`__
+# ```run_store_server.py`` <https://github.com/dmlc/dgl/blob/master/examples/mxnet/sampling/run_store_server.py>`__
 # that runs the graph store server and loads graph data. For example, the
 # following command runs a graph store server that loads the reddit
 # dataset and is configured to run with four trainers.
@@ -335,6 +335,27 @@ Large-Scale Training of Graph Neural Networks
 #
 # |image2|
 #
+# Scale to giant graphs
+# ---------------------
+#
+# Finally, we would like to demonstrate the scalability of DGL with giant
+# synthetic graphs. We create three large power-law graphs with
+# `RMAT <http://www.cs.cmu.edu/~christos/PUBLICATIONS/siam04.pdf>`__. Each
+# node is associated with 100 features and we compute node embeddings with
+# 64 dimensions. Below shows the training speed and memory consumption of
+# GCN with neighbor sampling.
+#
+# ====== ====== ================== ===========
+# #Nodes #Edges Time per epoch (s) Memory (GB)
+# ====== ====== ================== ===========
+# 5M     250M   4.7                8
+# 50M    2.5B   46                 75
+# 500M   25B    505                740
+# ====== ====== ================== ===========
+#
+# We can see that DGL can scale to graphs with up to 500M nodes and 25B
+# edges.
+#
 # .. |image0| image:: https://s3.us-east-2.amazonaws.com/dgl.ai/tutorial/sampling/arch.png
 # .. |image1| image:: https://s3.us-east-2.amazonaws.com/dgl.ai/tutorial/sampling/NUMA_speedup.png
 # .. |image2| image:: https://s3.us-east-2.amazonaws.com/dgl.ai/tutorial/sampling/whole_speedup.png

tutorials/models/5_giant_graph/README.txt

@@ -10,4 +10,9 @@ Training on giant graphs
   <https://github.com/dmlc/dgl/tree/master/examples/pytorch/sampling>`__:
   we can perform neighbor sampling and control-variate sampling to train
   graph convolution networks and its variants on a giant graph.
-
+* **Scale to giant graphs** `[tutorial] <5_giant_graph/2_giant.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 provide two components (graph store and distributed sampler) to scale to
+  graphs with hundreds of millions of nodes.