diff --git a/docs/cugraph-docs/source/graph_support/DGL_support.md b/docs/cugraph-docs/source/graph_support/DGL_support.md index ba9a28e..18c4c2f 100644 --- a/docs/cugraph-docs/source/graph_support/DGL_support.md +++ b/docs/cugraph-docs/source/graph_support/DGL_support.md @@ -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 diff --git a/docs/cugraph-docs/source/graph_support/PyG_support.md b/docs/cugraph-docs/source/graph_support/PyG_support.md index b57ce7f..7d2bb93 100644 --- a/docs/cugraph-docs/source/graph_support/PyG_support.md +++ b/docs/cugraph-docs/source/graph_support/PyG_support.md @@ -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. + + diff --git a/docs/cugraph-docs/source/graph_support/algorithms.md b/docs/cugraph-docs/source/graph_support/algorithms.md index 29cf23e..0b30fce 100644 --- a/docs/cugraph-docs/source/graph_support/algorithms.md +++ b/docs/cugraph-docs/source/graph_support/algorithms.md @@ -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** | | diff --git a/docs/cugraph-docs/source/graph_support/gnn_support.rst b/docs/cugraph-docs/source/graph_support/gnn_support.rst index 7158662..666afe8 100644 --- a/docs/cugraph-docs/source/graph_support/gnn_support.rst +++ b/docs/cugraph-docs/source/graph_support/gnn_support.rst @@ -2,6 +2,8 @@ Graph Neural Network Support ============================ +Here is a talk that explains `Training GNNs at Internet Scale using cuGraph and WholeGraph `_ + .. toctree:: :maxdepth: 2 @@ -9,3 +11,12 @@ Graph Neural Network Support 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 `_ + * `Getting Started with Large-Scale GNNs using cuGraph Packages for DGL and PyG `_ + + + + diff --git a/docs/cugraph-docs/source/graph_support/wholegraph_support.md b/docs/cugraph-docs/source/graph_support/wholegraph_support.md index d1c5eaf..55c5acf 100644 --- a/docs/cugraph-docs/source/graph_support/wholegraph_support.md +++ b/docs/cugraph-docs/source/graph_support/wholegraph_support.md @@ -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 diff --git a/docs/cugraph-docs/source/index.rst b/docs/cugraph-docs/source/index.rst index d210cf8..2ad48fc 100644 --- a/docs/cugraph-docs/source/index.rst +++ b/docs/cugraph-docs/source/index.rst @@ -45,10 +45,8 @@ Check out `zero code change accelerated NetworkX `_. If yo Getting started with cuGraph ---------------------------- -Required hardware/software for cuGraph and `RAPIDS `_ - * NVIDIA GPU, Volta architecture or later, with `compute capability 7.0+ `_ - * 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 `_ + ++++++++++++ Installation diff --git a/docs/cugraph-docs/source/references/cugraph_ref.md b/docs/cugraph-docs/source/references/cugraph_ref.md index 845436a..ea3a1eb 100644 --- a/docs/cugraph-docs/source/references/cugraph_ref.md +++ b/docs/cugraph-docs/source/references/cugraph_ref.md @@ -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 -

+- 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 \ No newline at end of file diff --git a/docs/cugraph-docs/source/references/datasets.md b/docs/cugraph-docs/source/references/datasets.md index 35234de..27b279f 100644 --- a/docs/cugraph-docs/source/references/datasets.md +++ b/docs/cugraph-docs/source/references/datasets.md @@ -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. \ No newline at end of file diff --git a/docs/cugraph-docs/source/tutorials/community_resources.md b/docs/cugraph-docs/source/tutorials/community_resources.md index 975f119..20bc054 100644 --- a/docs/cugraph-docs/source/tutorials/community_resources.md +++ b/docs/cugraph-docs/source/tutorials/community_resources.md @@ -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) diff --git a/docs/cugraph-docs/source/tutorials/cugraph_blogs.rst b/docs/cugraph-docs/source/tutorials/cugraph_blogs.rst index 337809d..d5faaa9 100644 --- a/docs/cugraph-docs/source/tutorials/cugraph_blogs.rst +++ b/docs/cugraph-docs/source/tutorials/cugraph_blogs.rst @@ -16,13 +16,12 @@ Coming Soon 2024 ------ * `NVIDIA cuGraph: 500x faster alternate for NetworkX for Graphs `_ - * `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 `_ * `Revolutionizing Graph Analytics: Next-Gen Architecture with NVIDIA cuGraph Acceleration `_ * `Accelerated, Production-Ready Graph Analytics for NetworkX Users `_ * `NetworkX Introduces Zero Code Change Acceleration Using NVIDIA cuGraph `_ * `NVIDIA cuGraph: Accelerate Graph Analytics with GPUs `_ -2023 + * `Enhanced Data Analytics: Integrating NVIDIA Rapids cuGraph with TigerGraph `_ + * `Insights, Techniques, and Evaluation for LLM-Driven Knowledge Graphs `_ ------ * `Intro to Graph Neural Networks with cuGraph-DGL `_ * `GTC 2023 Ask the Experts Q&A `_ @@ -65,6 +64,9 @@ Coming Soon Media =============== + * `NetworkX GPU Acceleration with cuGraph in Python ` + * `NVIDIA RAPIDS cuGraph : GPU acceleration for NetworkX, Graph Analytics ` + * `Accelerating Graph Analysis on GPUs ` * `Nvidia Rapids cuGraph: Making graph analysis ubiquitous `_ * `RAPIDS cuGraph – Accelerating all your Graph needs `_ diff --git a/docs/cugraph-docs/source/tutorials/cugraph_notebooks.md b/docs/cugraph-docs/source/tutorials/cugraph_notebooks.md index 6d7840d..bef39cb 100644 --- a/docs/cugraph-docs/source/tutorials/cugraph_notebooks.md +++ b/docs/cugraph-docs/source/tutorials/cugraph_notebooks.md @@ -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)
[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)
[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 diff --git a/docs/cugraph-docs/source/tutorials/how_to_guides.md b/docs/cugraph-docs/source/tutorials/how_to_guides.md index 998957a..afdcb15 100644 --- a/docs/cugraph-docs/source/tutorials/how_to_guides.md +++ b/docs/cugraph-docs/source/tutorials/how_to_guides.md @@ -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