rapidsai · GPUtester · Jan 27, 2025 · Jan 27, 2025
@@ -2,7 +2,7 @@
 
 ## Description
 
-[RAPIDS](https://rapids.ai) cugraph_dgl provides a duck-typed version of the [DGLGraph](https://docs.dgl.ai/api/python/dgl.DGLGraph.html#dgl.DGLGraph) class, which uses cugraph for storing graph structure and node/edge feature data.  Using cugraph as the backend allows DGL users to access a collection of GPU accelerated algorithms for graph analytics, such as centrality computation and community detection.
+[RAPIDS cugraph_dgl](https://github.com/rapidsai/cugraph-gnn/blob/branch-25.02/python/cugraph-dgl/README.md) provides a duck-typed version of the [DGLGraph](https://docs.dgl.ai/api/python/dgl.DGLGraph.html#dgl.DGLGraph) class, which uses cugraph for storing graph structure and node/edge feature data.  Using cugraph as the backend allows DGL users to access a collection of GPU accelerated algorithms for graph analytics, such as centrality computation and community detection.
 
 ## Conda
 
@@ -53,7 +53,7 @@ num_workers=0)
 ```
 
 ___
-Copyright (c) 2023, NVIDIA CORPORATION.
+Copyright (c) 2023-2025, NVIDIA CORPORATION.
 
 Licensed under the Apache License, Version 2.0 (the "License");  you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
 

@@ -1,3 +1,5 @@
 # cugraph_pyg
 
-[RAPIDS](https://rapids.ai) cugraph_pyg enables the ability to use cugraph graph storage and sampling with PyTorch Geometric (PyG).  PyG users will have access to cuGraph through the PyG GraphStore, FeatureStore, and Sampler interfaces.
+[cugraph_pyg](https://github.com/rapidsai/cugraph-gnn/tree/main/python/cugraph-pyg/cugraph_pyg) enables the ability to use cugraph graph storage and sampling with PyTorch Geometric (PyG).  PyG users will have access to cuGraph through the PyG GraphStore, FeatureStore, and Sampler interfaces.
+
+
@@ -55,7 +55,7 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M
 |                   | [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb)                | __Multi-GPU__ | [C++ README](./cpp_algorithms/centrality_cpp.html#Pagerank)                |
 |                   | [Personal Pagerank]()                  | __Multi-GPU__ | [C++ README](./cpp_algorithms/centrality_cpp.html#Personalized-Pagerank)   |
 |                   | [HITS](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/HITS.ipynb)        | __Multi-GPU__ |                |
-| [Link Prediction](algorithms/Similarity.html)   |                                    |                     |                                                                 |
+| [Link Prediction](./Similarity.html)   |                                    |                     |                                                                 |
 |                   | [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb)                 | __Multi-GPU__      | Directed graph only                         |
 |                   | [Weighted Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb)        | Single-GPU          |                                                                 |
 |                   | [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb)                 | **Multi-GPU** |                                                   |

@@ -2,10 +2,21 @@
 Graph Neural Network Support
 ============================
 
+Here is a talk that explains `Training GNNs at Internet Scale using cuGraph and WholeGraph <https://www.youtube.com/watch?v=rS-bcWm3jQo&t=2762s>`_
+
 
 .. toctree::
    :maxdepth: 2
 
    PyG_support.md
    DGL_support.md
    wholegraph_support.md
+
+Blogs to explain how RAPIDS cuGraph supports GNN'S
+==================================================
+  * `Optimizing Memory and Retrieval for Graph Neural Networks with WholeGraph, Part 1 <https://developer.nvidia.com/blog/optimizing-memory-and-retrieval-for-graph-neural-networks-with-wholegraph-part-1/>`_
+  * `Getting Started with Large-Scale GNNs using cuGraph Packages for DGL and PyG <https://www.nvidia.com/en-us/on-demand/session/gtc24-dlit61291/?playlistId=playList-108242b0-35ac-4765-9796-d6961cb026c4>`_
+
+
+
+
@@ -1,4 +1,4 @@
 # WholeGraph
 
-[RAPIDS](https://rapids.ai) [WholeGraph](https://github.com/rapidsai/wholegraph) is designed to help train large-scale Graph Neural Networks(GNN).
-Please see [WholeGraph Introduction](https://github.com/rapidsai/wholegraph/blob/main/README.md) for more details
+[RAPIDS WholeGraph](https://github.com/rapidsai/cugraph-gnn/blob/main/python/pylibwholegraph/) is designed to help train large-scale Graph Neural Networks(GNN).
+Please see [WholeGraph Introduction](https://github.com/rapidsai/cugraph-gnn/blob/main/README.md) for more details
@@ -45,10 +45,8 @@ Check out `zero code change accelerated NetworkX <nx_cugraph/index.rst>`_. If yo
 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>`_
- * CUDA 11.2-11.8, 12.0-12.5
- * Python version 3.10, 3.11, or 3.12
+Required hardware/software for `cuGraph and RAPIDS <https://docs.rapids.ai/install/#system-req>`_
+
 
 ++++++++++++
 Installation

@@ -41,5 +41,9 @@
 ## Other Papers
 - Hricik, T., Bader, D., & Green, O. (2020, September). *Using RAPIDS AI to Accelerate Graph Data Science Workflows*. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE.
 
+- Tang, Jiawei & Gao, Min & Xiao, Yu & Li, Cong & Chen, Yang. (2024). EGGPU: Enabling Efficient Large-Scale Network Analysis with Consumer-Grade GPUs. 10.1145/3698387.3699997. 
+https://www.researchgate.net/publication/384925237_EGGPU_Enabling_Efficient_Large-Scale_Network_Analysis_with_Consumer-Grade_GPUs
 
-</br></br>
+- N. Keskes1. GPU Acceleration of Graph Algorithms in NextVision: A Seismic Data Interpretation Tool
+Eighth EAGE High Performance Computing Workshop, Sep 2024, Volume 2024, p.1 - 3
+https://www.earthdoc.org/content/papers/10.3997/2214-4609.2024636024
@@ -1,21 +1,47 @@
 # Data Sets
 
-karate
+
+## karate
   - W. W. Zachary, *An information flow model for conflict and fission in small groups*, Journal of Anthropological Research 33, 452-473 (1977).
-dolphins
+## dolphins
   - D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson,
     *The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations*,
     Behavioral Ecology and Sociobiology 54, 396-405 (2003).
-netscience
+## netscience
   - M. E. J. Newman,
     *Finding community structure in networks using the eigenvectors of matrices*,
     Preprint physics/0605087 (2006).
-email-Eu-core
+## email-Eu-core
   - Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich.
     *Local Higher-order Graph Clustering.*
     In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017.
   - J. Leskovec, J. Kleinberg and C. Faloutsos.
     *Graph Evolution: Densification and Shrinking Diameters*.
     ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 1(1), 2007. http://www.cs.cmu.edu/~jure/pubs/powergrowth-tkdd.pdf
-polbooks
-  - V. Krebs, unpublished, http://www.orgnet.com/.
+## polbooks
+  - V. Krebs, "The political books network", unpublished, https://doi.org/10.2307/40124305 [@sci-hub]
+## amazon
+  - J. Leskovec, L. Adamic and B. Adamic. The Dynamics of Viral Marketing.
+    ACM Transactions on the Web (ACM TWEB), 1(1), 2007. https://snap.stanford.edu/data/amazon0302.txt.gz
+## cit-patents
+  - J. Leskovec, J. Kleinberg and C. Faloutsos. Graphs over Time Densification Laws, Shrinking Diameters and Possible Explanations.
+  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2005.
+## cyber
+  - Moustafa, Nour. Designing an online and reliable statistical anomaly detection
+  framework for dealing with large high-speed network traffic. Diss. University
+  of New South Wales, Canberra, Australia, 2017.
+## dining_prefs
+  - J. L. Moreno (1960). The Sociometry Reader. The Free Press, Glencoe, Illinois, pg.35
+## europe_osm
+  - Rossi, Ryan. Ahmed, Nesreen. The Network Data Respository with Interactive Graph Analytics and Visualization.
+## hollywood
+..- The WebGraph Framework I Compression Techniques, Paolo Boldi
+  and Sebastiano Vigna, Proc. of the Thirteenth International
+  World Wide Web Conference (WWW 2004), 2004, Manhattan, USA,
+  pp. 595--601, ACM Press.
+## soc-livejournal1
+  - L. Backstrom, D. Huttenlocher, J. Kleinberg, X. Lan. Group Formation in
+  Large Social Networks Membership, Growth, and Evolution. KDD, 2006.
+## soc-twitter-2010
+  - J. Yang, J. Leskovec. Temporal Variation in Online Media. ACM Intl.
+  Conf. on Web Search and Data Mining (WSDM '11), 2011.
@@ -1,4 +1,4 @@
 # Commmunity Resources
-[Rapids Community Repository](https://github.com/rapidsai-community/notebooks-contrib)
-[RAPIDS Containers on Docker Hub](https://catalog.ngc.nvidia.com/containers)
-[RAPIDS PyTorch Container in Docker](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pyg)
+* [Rapids Community Repository](https://github.com/rapidsai-community/notebooks-contrib)
+* [RAPIDS Containers on Docker Hub](https://catalog.ngc.nvidia.com/containers)
+* [RAPIDS PyTorch Container in Docker](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pyg)
@@ -16,13 +16,12 @@ Coming Soon
 2024
 ------
   * `NVIDIA cuGraph: 500x faster alternate for NetworkX for Graphs <https://medium.com/data-science-in-your-pocket/nvidia-cugraph-500x-faster-alternate-for-networkx-for-graphs-ef7e2ad9fbda>`_
-  * `Optimizing Memory and Retrieval for Graph Neural Networks with WholeGraph, Part 1 <https://developer.nvidia.com/blog/optimizing-memory-and-retrieval-for-graph-neural-networks-with-wholegraph-part-1/>`_
-  * `Getting Started with Large-Scale GNNs using cuGraph Packages for DGL and PyG <https://www.nvidia.com/en-us/on-demand/session/gtc24-dlit61291/?playlistId=playList-108242b0-35ac-4765-9796-d6961cb026c4>`_
   * `Revolutionizing Graph Analytics: Next-Gen Architecture with NVIDIA cuGraph Acceleration <https://developer.nvidia.com/blog/revolutionizing-graph-analytics-next-gen-architecture-with-nvidia-cugraph-acceleration/>`_
   * `Accelerated, Production-Ready Graph Analytics for NetworkX Users <https://developer.nvidia.com/blog/accelerated-production-ready-graph-analytics-for-networkx-users/>`_
   * `NetworkX Introduces Zero Code Change Acceleration Using NVIDIA cuGraph <https://developer.nvidia.com/blog/networkx-introduces-zero-code-change-acceleration-using-nvidia-cugraph/>`_
   * `NVIDIA cuGraph: Accelerate Graph Analytics with GPUs <https://medium.com/data-science-in-your-pocket/nvidia-cugraph-accelerate-graph-analytics-with-gpus-4d809345040f>`_
-2023
+  * `Enhanced Data Analytics: Integrating NVIDIA Rapids cuGraph with TigerGraph <https://www.tigergraph.com/blog/tigergraph-copilot-enters-public-alpha-release-copy/>`_
+  * `Insights, Techniques, and Evaluation for LLM-Driven Knowledge Graphs <https://developer.nvidia.com/blog/insights-techniques-and-evaluation-for-llm-driven-knowledge-graphs/>`_
 ------
   * `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>`_
@@ -65,6 +64,9 @@ Coming Soon
 
 Media
 ===============
+  * `NetworkX GPU Acceleration with cuGraph in Python <https://www.youtube.com/watch?v=92OxVC-1aiE>`
+  * `NVIDIA RAPIDS cuGraph : GPU acceleration for NetworkX, Graph Analytics <https://www.youtube.com/watch?v=FBxAIoH49Xc>`
+  * `Accelerating Graph Analysis on GPUs <https://www.youtube.com/watch?v=piNP2LbfMFk>`
   * `Nvidia Rapids cuGraph: Making graph analysis ubiquitous <https://www.zdnet.com/article/nvidia-rapids-cugraph-making-graph-analysis-ubiquitous/>`_
   * `RAPIDS cuGraph – Accelerating all your Graph needs <https://www.youtube.com/watch?v=kAw7-IGH9N4>`_
 

@@ -40,8 +40,8 @@ Layout            |
 |                 | [BFS](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/traversal/BFS.ipynb)                                   | Compute the Breadth First Search path from a starting vertex to every other vertex in a graph |
 |                 | [SSSP](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/traversal/SSSP.ipynb)                                 | Single Source Shortest Path  - compute the shortest path from a starting vertex to every other vertex |
 | Structure       |                                                              |                                                              |
-|                 | [Renumbering](algorithms/structure/Renumber.ipynb) <br> [Renumbering 2](algorithms/structure/Renumber-2.ipynb) | Renumber the vertex IDs in a graph (two sample notebooks)    |
-|                 | [Symmetrize](algorithms/structure/Symmetrize.ipynb)                     | Symmetrize the edges in a graph                              |
+|                 | [Renumbering](algorithms/structure/Renumber.ipynb) <br> [Renumbering 2](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/structure/Renumber-2.ipynb) | Renumber the vertex IDs in a graph (two sample notebooks)    |
+|                 | [Symmetrize](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/structure/Symmetrize.ipynb)                     | Symmetrize the edges in a graph                              |
 
 
 ## RAPIDS notebooks
@@ -61,7 +61,7 @@ Running the example in these notebooks requires:
 
 ## Copyright
 
-Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
+Copyright (c) 2019-2025, NVIDIA CORPORATION.  All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");  you may not use this file except in compliance with the License.  You may obtain a copy of the License at
 

@@ -1,8 +1,6 @@
 # How To Guides
 - [Basic use of cuGraph](./basic_cugraph.md)
-- Property graph with analytic flow
 - GNN – model building
-- cuGraph Service – client/server setup and use (ucx)
 - MNMG Graph – dask, rmm basics and examples
 - Pylibcugraph – why and how
 - Cugraph for C, C++ users