fixed section headers and added content to cugraph landing page

docs/cugraph/source/index.rst

@@ -11,15 +11,15 @@ Introduction
 cuGraph is a library of graph algorithms that seamlessly integrates into the
 RAPIDS data science ecosystem and allows the data scientist to easily call
 graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or
-even CuPy or SciPy sparse Matrices. A major integration effort with NetworkX is focused on
-**zero code change** use of nx-cuGraph as a backend for NetworkX calls. This allows a near seamless
-transition to GPU accelerated graph analytics.
+even CuPy or SciPy sparse Matrices. Our major integration effort with NetworkX allows
+**zero code change** use of nx-cuGraph as a backend for NetworkX calls. This offers a near seamless
+transition to GPU accelerated graph analytics to NetworkX users with access to a supported GPU.
 
 Getting started with cuGraph
 
 Required hardware/software for cuGraph and `RAPIDS <https://docs.rapids.ai/user-guide>`_
  * NVIDIA GPU, Volta architecture or later, with `compute capability <https://developer.nvidia.com/cuda-gpus> 7.0+`_
- * CUDA 11.4-11.8, 12.0-12.5
+ * CUDA 11.2-11.8, 12.0-12.5
  * Python version 3.10, 3.11, or 3.12
  * NetworkX version 3.0 or newer in order to use use the nx-cuGraph backend. Version 3.3 is required to use `NetworkX Configs <https://networkx.org/documentation/stable/reference/backends.html#module-networkx.utils.configs>`_ `see below <#cugraph-using-networkx-code>`_.
 
@@ -44,19 +44,18 @@ This includes several ways to set up cuGraph
   * `pip <https://docs.rapids.ai/install#wsl-pip>`_
 
 
-
-
 Build From Source
 
-To build from source, check each RAPIDS GitHub README for set up and build instructions. Further links are provided in the `selector tool <https://docs.rapids.ai/install#selector>`_. If additional help is needed reach out on our `Slack Channel <https://rapids-goai.slack.com/archives/C5E06F4DC>`_.
+To build from source, check each RAPIDS GitHub README for set up and build instructions. Further links are provided in the `selector tool <https://docs.rapids.ai/install#selector>`_. 
+If additional help is needed reach out on our `Slack Channel <https://rapids-goai.slack.com/archives/C5E06F4DC>`_.
 
 CuGraph Using NetworkX Code
 While the steps above are required to use the full suite of cuGraph graph analytics, cuGraph is now supported as a NetworkX backend using `nx-cugraph <https://docs.rapids.ai/api/cugraph/nightly/nx_cugraph/nx_cugraph/>_.
 Nx-cugraph offers those with existing NetworkX code, a **zero code change** option with a growing list of supported algorithms.
 
 
  Cugraph API Example
-Coming soon !
+
 
 
 Until then, `the cuGraph notebook repository <https://github.com/rapidsai/cugraph/blob/main/notebooks/README.md>`_ 
@@ -66,18 +65,10 @@ A simple example of `testing the degree centrality algorithm <https://github.com
 is a good place to start. Some of these show `multi-GPU tests/examples <https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py>`_ with larger data sets  as well.
 
 .. toctree::
-  :maxdepth: 1
-  :caption: cuGraph documentation Contents:
-
-  basics/index
-  nx_cugraph/index
-  installation/index
-  tutorials/index
-  graph_support/index
-  wholegraph/index
-  references/index
-  api_docs/index
+   :maxdepth: 2
 
+   top_toc
+
 Indices and tables
 ==================
 

docs/cugraph/source/top_toc.rst

@@ -0,0 +1,13 @@
+.. toctree::
+  :maxdepth: 2
+  :caption: cuGraph documentation Contents:
+  :name: top_toc
+
+  basics/index
+  nx_cugraph/index
+  installation/index
+  tutorials/index
+  graph_support/index
+  wholegraph/index
+  references/index
+  api_docs/index