first changes to docs blogs and nx docs

docs/cugraph/source/basics/index.rst

@@ -8,4 +8,5 @@ Basics
 
    cugraph_intro
    nx_transition
+   nx_cuGraph_algos
    cugraph_cascading

docs/cugraph/source/basics/nx_transition.rst

@@ -1,9 +1,8 @@
 **************************************
-NetworkX Compatibility and Transition
+NetworkX by calling cuGraph Algorithms 
 **************************************
 
-*Note: this is a work in progress and will be updatred and changed as we better flesh out 
-compatibility issues*
+*Note: This behavior is still supported but will soon be deprecated. Going forward, using nx_cugraph as a NetworkX backend will be the the primary method.*
 
 One of the goals of RAPIDS cuGraph is to mimic the NetworkX API to simplify
 the transition to accelerated GPU data science.  However, graph analysis,
@@ -18,8 +17,11 @@ But sometimes it is easier to replace just a portion.
 Last Update
 ###########
 
-Last Update:	Oct 14th, 2020
-Release:		0.16
+Last Update:	February 7th, 2024
+Release:		24.04
+
+**CuGraph is now a registered backend for networkX. This is described in the blog on 
+`Accelerating NetworkX on NVIDIA GPUs for High Performance Graph Analytics`**
 
 Information on `NetworkX <https://networkx.github.io/documentation/stable/index.html>`_
 

docs/cugraph/source/tutorials/cugraph_blogs.rst

@@ -9,6 +9,17 @@ Here, we've selected just a few that are of particular interest to cuGraph users
 
 Blogs & Conferences
 ====================
+2024
+------
+Coming Soon
+
+2023
+------
+  * `Intro to Graph Neural Networks with cuGraph-DGL <https://medium.com/rapids-ai/introduction-to-graph-neural-networks-with-cugraph-dgl-64c632e9cc52>`_
+  * `GTC 2023 Ask the Experts Q&A <https://forums.developer.nvidia.com/c/blogs-events/connect-with-experts/ama-cugraph/652?ncid=em-even-260150-vt33#cid=dev03_em-even_en-us>`_
+  * `Accelerating NetworkX on NVIDIA GPUs for High Performance Graph Analytics <https://developer.nvidia.com/blog/accelerating-networkx-on-nvidia-gpus-for-high-performance-graph-analytics/>`_
+  * `Introduction to Graph Neural Networks with NVIDIA cuGraph-DGL <https://developer.nvidia.com/blog/introduction-to-graph-neural-networks-with-nvidia-cugraph-dgl/>`_
+  * `Supercharge Graph Analytics at Scale with GPU-CPU Fusion for 100x Performance <https://developer.nvidia.com/blog/supercharge-graph-analytics-at-scale-with-gpu-cpu-fusion-for-100x-performance/>`_
 2022
 ------
   * `GTC: State of cuGraph  (video & slides) <https://www.nvidia.com/gtc/session-catalog/?search=cuGraph&tab.scheduledorondemand=1583520458947001NJiE&search=cuGraph#/session/1635793340204001n4p2>`_
@@ -50,6 +61,8 @@ Media
 Academic Papers
 ===============
 
+ * Seunghwa Kang, Chuck Hastings, Joe Eaton, Brad Rees `cuGraph C++ primitives: vertex/edge-centric building blocks for parallel graph computing <https://ieeexplore.ieee.org/abstract/document/10196665>`_
+
  * Alex Fender, Brad Rees, Joe Eaton (2022) `Massive Graph Analytics <https://books.google.com/books?hl=en&lr=&id=QspxEAAAQBAJ&oi=fnd&pg=PT8&dq=book:%22Massive+Graph+Analytics%22&ots=3HAGJ0njKO&sig=8e4v0azmzA6LTQNUNgPw-uTLkoc#v=onepage&q&f=false>`_  Bader, D. (Editor) CRC Press
 
  * S Kang, A. Fender, J. Eaton, B. Rees:`Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters`. In IEEE HPEC, Sep. 2020
@@ -58,6 +71,8 @@ Academic Papers
 
  * Richardson, B., Rees, B., Drabas, T., Oldridge, E., Bader, D. A., & Allen, R. (2020, August). Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3503-3504).
 
+ * A Gondhalekar, P Sathre, W Feng `Hybrid CPU-GPU Implementation of Edge-Connected Jaccard Similarity in Graph Datasets <https://sc23.supercomputing.org/proceedings/tech_poster/poster_files/rpost221s3-file3.pdf>`_
+
 
 Other Blogs
 ========================