[Doc] Graph neural network and its variant Edit for grammar and style (#992)

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* Update tutorials/models/1_gnn/README.txt
Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update tutorials/models/1_gnn/README.txt
Co-Authored-By: Aaron Markham <markhama@amazon.com>

tutorials/models/1_gnn/README.txt

 .. _tutorials1-index:
 
-Graph Neural Network and its variant
+Graph neural networks and its variants
 ====================================
 
-* **GCN** `[paper] <https://arxiv.org/abs/1609.02907>`__ `[tutorial]
+* **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 vanilla GCN. The tutorial covers the basic uses of DGL APIs.
+  This is the most basic GCN. The tutorial covers the basic uses of DGL APIs.
 
-* **GAT** `[paper] <https://arxiv.org/abs/1710.10903>`__ `[tutorial]
+* **Graph attention network (GAT)** `[research paper] <https://arxiv.org/abs/1710.10903>`__ `[tutorial]
   <1_gnn/9_gat.html>`__ `[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
+  GAT extends the GCN functionality by deploying multi-head attention
+  among neighborhood of a node. This greatly enhances the capacity and
   expressiveness of the model.
 
-* **R-GCN** `[paper] <https://arxiv.org/abs/1703.06103>`__ `[tutorial]
+* **Relational-GCN** `[research paper] <https://arxiv.org/abs/1703.06103>`__ `[tutorial]
   <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.
+  Relational-GCN allows multiple edges among two entities of a
+  graph. Edges with distinct relationships are encoded differently. 
 
-* **LGNN** `[paper] <https://arxiv.org/abs/1705.08415>`__ `[tutorial]
+* **Line graph neural network (LGNN)** `[research paper] <https://arxiv.org/abs/1705.08415>`__ `[tutorial]
   <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
+  This network focuses on community detection by inspecting graph structures. It
   uses representations of both the original graph and its line-graph
-  companion. In addition to demonstrate how an algorithm can harness multiple
-  graphs, our implementation shows how one can judiciously mix vanilla tensor
-  operation, sparse-matrix tensor operations, along with message-passing with
+  companion. In addition to demonstrating how an algorithm can harness multiple
+  graphs, this implementation shows how you can judiciously mix simple tensor
+  operations and sparse-matrix tensor operations, along with message-passing with
   DGL.
 
-* **SSE** `[paper] <http://proceedings.mlr.press/v80/dai18a/dai18a.pdf>`__ `[tutorial]
+* **Stochastic steady-state embedding (SSE)** `[research paper] <http://proceedings.mlr.press/v80/dai18a/dai18a.pdf>`__ `[tutorial]
   <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
-  system: sampling to guarantee asymptotic convergence while lowering the
-  complexity, and batching across samples for maximum parallelism.
+  SSE is an example to illustrate the co-design of both algorithm and
+  system. Sampling to guarantee asymptotic convergence while lowering
+  complexity and batching across samples for maximum parallelism. The emphasis 
+  here is that a giant graph that cannot fit comfortably on one GPU card.