Fix links, formatting, etc

rapidsai · Oct 4, 2024 · ef9a379 · ef9a379
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diff --git a/docs/cugraph/source/nx_cugraph/benchmarks.md b/docs/cugraph/source/nx_cugraph/benchmarks.md
@@ -15,7 +15,7 @@ class="title-ref"></span></figcaption>
 
 ## Reproducing Benchmarks
 
-Below are the steps to reproduce the results on your workstation. These are documented in this [README](https://github.com/rapidsai/cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based).
+Below are the steps to reproduce the results on your own.
 
 1. Clone the latest <https://github.com/rapidsai/cugraph>
 
@@ -25,4 +25,4 @@ Below are the steps to reproduce the results on your workstation. These are docu
 
 4. Install the latest `nx-cugraph` by following the [guide](installation.md)
 
-5. Follow the instructions written in the README here: `cugraph/benchmarks/nx-cugraph/pytest-based/`
+5. Follow the instructions written in the README [here](https://github.com/rapidsai/cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based)
diff --git a/docs/cugraph/source/nx_cugraph/how-it-works.md b/docs/cugraph/source/nx_cugraph/how-it-works.md
@@ -11,6 +11,8 @@ While NetworkX is a pure-Python implementation with minimal to no dependencies,
 
 ## Enabling nx-cugraph
 
+It is recommended to use `networkx>=3.4` for optimal compatibility, but `nx-cugraph` will work with `3.0+`.
+
 NetworkX will use `nx-cugraph` as the backend if any of the following are used:
 
 ### `NX_CUGRAPH_AUTOCONFIG` environment variable.

diff --git a/docs/cugraph/source/nx_cugraph/supported-algorithms.rst b/docs/cugraph/source/nx_cugraph/supported-algorithms.rst
@@ -2,7 +2,7 @@ Supported Algorithms
 =====================
 
 The nx-cugraph backend to NetworkX connects
-`pylibcugraph <../../readme_pages/pylibcugraph.md>`_ (cuGraph's low-level Python
+`pylibcugraph <https://github.com/rapidsai/cugraph/blob/HEAD/readme_pages/pylibcugraph.md>`_ (cuGraph's low-level Python
 interface to its CUDA-based graph analytics library) and
 `CuPy <https://cupy.dev/>`_ (a GPU-accelerated array library) to NetworkX's
 familiar and easy-to-use API.
@@ -209,6 +209,40 @@ Algorithms
 | is_tree             |
 +---------------------+
 
+
+Utilities
+-------
+
++-------------------------+
+| **Classes**             |
++=========================+
+| is_negatively_weighted  |
++-------------------------+
+
++----------------------+
+| **Convert**          |
++======================+
+| from_dict_of_lists   |
++----------------------+
+| to_dict_of_lists     |
++----------------------+
+
++--------------------------+
+| **Convert Matrix**       |
++==========================+
+| from_pandas_edgelist     |
++--------------------------+
+| from_scipy_sparse_array  |
++--------------------------+
+
++-----------------------------------+
+| **Relabel**                       |
++===================================+
+| convert_node_labels_to_integers   |
++-----------------------------------+
+| relabel_nodes                     |
++-----------------------------------+
+
 Generators
 ------------
 
@@ -316,39 +350,6 @@ Generators
 | les_miserables_graph          |
 +-------------------------------+
 
-Other
--------
-
-+-------------------------+
-| **Classes**             |
-+=========================+
-| is_negatively_weighted  |
-+-------------------------+
-
-+----------------------+
-| **Convert**          |
-+======================+
-| from_dict_of_lists   |
-+----------------------+
-| to_dict_of_lists     |
-+----------------------+
-
-+--------------------------+
-| **Convert Matrix**       |
-+==========================+
-| from_pandas_edgelist     |
-+--------------------------+
-| from_scipy_sparse_array  |
-+--------------------------+
-
-+-----------------------------------+
-| **Relabel**                       |
-+===================================+
-| convert_node_labels_to_integers   |
-+-----------------------------------+
-| relabel_nodes                     |
-+-----------------------------------+
-
 
 To request nx-cugraph backend support for a NetworkX API that is not listed
 above, visit the `cuGraph GitHub repo <https://github.com/rapidsai/cugraph>`_.
diff --git a/readme_pages/pylibcugraph.md b/readme_pages/pylibcugraph.md
@@ -4,7 +4,7 @@
 </h1>
 <h1 align="left";>
   <br>
-CuGraph pylibcugraph
+cuGraph pylibcugraph
 </h1>
 
 Part of [RAPIDS](https://rapids.ai) cuGraph, pylibcugraph is a wrapper around the cuGraph C API. It is aimed more at integrators instead of algorithm writers or end users like Data Scientists. Most of the cuGraph python API uses pylibcugraph to efficiently run algorithms by removing much of the overhead of the python-centric implementation, relying more on cython instead. Pylibcugraph is intended for applications that require a tighter integration with cuGraph at the Python layer with fewer dependencies.