added original toc and fixed up landing page

docs/cugraph/source/index.rst

@@ -10,20 +10,18 @@ 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. Our major integration effort with NetworkX
-allows for **zero code change** GPU acceleration through the use of the
-nx-cugraph backend. NetworkX and the nx-cugraph backend offer a seamless
-transition to GPU accelerated graph analytics for NetworkX users with access to
-a supported GPU.
+graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or
+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 7.0+ <https://developer.nvidia.com/cuda-gpus>`_
+ * NVIDIA GPU, Volta architecture or later, with `compute capability <https://developer.nvidia.com/cuda-gpus>`_ 7.0+
  * 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. NetworkX version 3.4 or newer is recommended. (`see below <nx_cugraph/index.rst>`_).
+ * 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>`_.
 
 Installation
 The latest RAPIDS System Requirements documentation is located `here <https://docs.rapids.ai/install#system-req>`_.
@@ -39,45 +37,56 @@ This includes several ways to set up cuGraph
 
 **Note: Windows use of RAPIDS depends on prior installation of** `WSL2 <https://learn.microsoft.com/en-us/windows/wsl/install>`_.
 
-* From Windows
+* From windows
 
-  * `Conda <https://docs.rapids.ai/install#wsl2-conda>`_
-  * `Docker <https://docs.rapids.ai/install#wsl2-docker>`_
-  * `pip <https://docs.rapids.ai/install#wsl2-pip>`_
+  * `Conda <https://docs.rapids.ai/install#wsl-conda>`_
+  * `Docker <https://docs.rapids.ai/install#wsl-docker>`_
+  * `pip <https://docs.rapids.ai/install#wsl-pip>`_
 
 
-cuGraph Using NetworkX Code
+Build From Source
 
-cuGraph is now available as a NetworkX backend using `nx-cugraph <https://rapids.ai/nx-cugraph/>`_.
-nx-cugraph offers NetworkX users a **zero code change** option to accelerate
-their existing NetworkX code using an NVIDIA GPU and cuGraph.
+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
 
  .. code-block:: python
 
+  # Import needed libraries
   import cugraph
   import cudf
-
+  
   # Create an instance of the popular Zachary Karate Club graph
   from cugraph.datasets import karate
   G = karate.get_graph()
 
   # Call cugraph.degree_centrality
   vertex_bc = cugraph.degree_centrality(G)
 
-There are several resources containing cuGraph examples, `the cuGraph notebook repository <https://github.com/rapidsai/cugraph/blob/HEAD/notebooks/README.md>`_
+
+There are several resources containing cuGraph examples, `the cuGraph notebook repository <https://github.com/rapidsai/cugraph/blob/main/notebooks/README.md>`_
 has many examples of loading graph data and running algorithms in Jupyter notebooks.
-The `cuGraph test code <https://github.com/rapidsai/cugraph/tree/main/python/cugraph/cugraph/tests>_` contain python scripts setting up and calling cuGraph algorithms.
-A simple example of `testing the degree centrality algorithm <https://github.com/rapidsai/cugraph/blob/HEAD/python/cugraph/cugraph/tests/centrality/test_degree_centrality.py>`_
-is a good place to start. Some of these show `multi-GPU tests/examples <https://github.com/rapidsai/cugraph/blob/HEAD/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py>`_ with larger data sets as well.
+The `cuGraph test code <https://github.com/rapidsai/cugraph/tree/main/python/cugraph/cugraph/tests>`_ contains python scripts setting up and calling cuGraph algorithms.
+A simple example of `testing the degree centrality algorithm <https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality.py>`_
+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: 2
 
-   top_toc
-
+  basics/index
+  nx_cugraph/index
+  installation/index
+  tutorials/index
+  graph_support/index
+  wholegraph/index
+  references/index
+  api_docs/index
+
 Indices and tables
 ==================