[Doc] Show MXNet examples (#318)

* update GNN tutorial.

* Update README.txt

tutorials/models/1_gnn/README.txt

@@ -4,26 +4,32 @@ Graph Neural Network and its variant
 ------------------------------------
 
 * **GCN** `[paper] <https://arxiv.org/abs/1609.02907>`__ `[tutorial]
-  <1_gnn/1_gcn.html>`__ `[code]
-  <https://github.com/dmlc/dgl/blob/master/examples/pytorch/gcn>`__:
+  <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 vanilla GCN. The tutorial covers the basic uses of DGL APIs.
 
-* **GAT** `[paper] <https://arxiv.org/abs/1710.10903>`__ `[code]
-  <https://github.com/dmlc/dgl/blob/master/examples/pytorch/gat>`__:
+* **GAT** `[paper] <https://arxiv.org/abs/1710.10903>`__ `[Pytorch code]
+  <https://github.com/dmlc/dgl/blob/master/examples/pytorch/gat>`__
+  `[MXNet code]
+  <https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat>`__:
   the key extension of GAT w.r.t vanilla GCN is deploying multi-head attention
   among neighborhood of a node, thus greatly enhances the capacity and
   expressiveness of the model.
 
 * **R-GCN** `[paper] <https://arxiv.org/abs/1703.06103>`__ `[tutorial]
-  <1_gnn/4_rgcn.html>`__ `[code]
-  <https://github.com/dmlc/dgl/tree/master/examples/pytorch/rgcn>`__:
+  <1_gnn/4_rgcn.html>`__ `[Pytorch code]
+  <https://github.com/dmlc/dgl/tree/master/examples/pytorch/rgcn>`__
+  `[MXNet code]
+  <https://github.com/dmlc/dgl/tree/master/examples/mxnet/rgcn>`__:
   the key difference of RGNN is to allow multi-edges among two entities of a
   graph, and edges with distinct relationships are encoded differently. This
   is an interesting extension of GCN that can have a lot of applications of
   its own.
 
 * **LGNN** `[paper] <https://arxiv.org/abs/1705.08415>`__ `[tutorial]
-  <1_gnn/6_line_graph.html>`__ `[code]
+  <1_gnn/6_line_graph.html>`__ `[Pytorch code]
   <https://github.com/dmlc/dgl/tree/master/examples/pytorch/line_graph>`__:
   this model focuses on community detection by inspecting graph structures. It
   uses representations of both the original graph and its line-graph
@@ -33,7 +39,7 @@ Graph Neural Network and its variant
   DGL.
 
 * **SSE** `[paper] <http://proceedings.mlr.press/v80/dai18a/dai18a.pdf>`__ `[tutorial]
-  <1_gnn/8_sse_mx.html>`__ `[code]
+  <1_gnn/8_sse_mx.html>`__ `[MXNet code]
   <https://github.com/dmlc/dgl/blob/master/examples/mxnet/sse>`__:
   the emphasize here is *giant* graph that cannot fit comfortably on one GPU
   card. SSE is an example to illustrate the co-design of both algorithm and