nx-cugraph_demo.ipynb

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      "gpuType": "A100",
      "machine_shape": "hm",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nv-rliu/accelerated-networkx-demo/blob/main/nx-cugraph_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NetworkX - Easy Graph Analytics\n",
        "\n",
        "NetworkX is the most popular library for graph analytics available in Python, or quite possibly any language. To illustrate this, NetworkX was downloaded more than 80 million times in September of 2024 alone, which is roughly 50 times more than the next most popular graph analytics library! [*](https://en.wikipedia.org/wiki/NetworkX) NetworkX has earned this popularity from its very easy-to-use API, the wealth of documentation and examples available, the large (and friendly) community behind it, and its easy installation which requires nothing more than python.\n",
        "\n",
        "However, NetworkX users are familiar with the tradeoff that comes with those benefits. The pure-Python implementation often results in poor performance when graph data starts to reach larger scales, limiting the usefulness of the library for many real-world problems.\n",
        "\n",
        "# Accelerated NetworkX - Easy (and fast!) Graph Analytics\n",
        "\n",
        "To address the performance problem, NetworkX 3.0 introduced a mechanism to dispatch algorithm calls to alternate implementations. The NetworkX Python API remains the same but NetworkX will use more capable algorithm implementations provided by one or more backends. This approach means users don't have to give up NetworkX -or even change their code- in order to take advantage of GPU performance."
      ],
      "metadata": {
        "id": "R2cpVp2WdOsp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's Get the Environment Setup\n",
        "This notebook will demonstrate NetworkX both with and without GPU acceleration provided by the `nx-cugraph` backend.\n",
        "\n",
        "`nx-cugraph` is available as a package installable using `pip`, `conda`, and [from source](https://github.com/rapidsai/nx-cugraph).  Before importing `networkx`, lets install `nx-cugraph` so it can be registered as an available backend by NetworkX when needed.  We'll use `pip` to install.\n",
        "\n",
        "NOTES:\n",
        "* `nx-cugraph` requires a compatible NVIDIA GPU, NVIDIA CUDA, its associated drivers, and a supported OS. Details about these and other installation prerequisites can be seen [here](https://docs.rapids.ai/install#system-req).\n",
        "* The `nx-cugraph` package is currently hosted by NVIDIA, therefore the `--extra-index-url` option must be used.\n",
        "* `nx-cugraph` is supported on specific 11.x and 12.x CUDA versions, and the major version number must be known in order to install the correct build (this is determined automatically when using `conda`).\n",
        "\n",
        "To find the CUDA major version on your system, run the following command:"
      ],
      "metadata": {
        "id": "xkg10FrNThrK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvcc --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NMFwzc1I95BS",
        "outputId": "0bac6601-0e31-4e1b-842c-61846cbadff7"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2023 NVIDIA Corporation\n",
            "Built on Tue_Aug_15_22:02:13_PDT_2023\n",
            "Cuda compilation tools, release 12.2, V12.2.140\n",
            "Build cuda_12.2.r12.2/compiler.33191640_0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above output we can see that we're using CUDA 12.x so we'll be installing `nx-cugraph-cu12`. If we were using CUDA 11.x the package name would be `nx-cugraph-cu11`. We'll also be adding `https://pypi.nvidia.com` as an `--extra-index-url`:"
      ],
      "metadata": {
        "id": "i91Yj-yZ-nGS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install nx-cugraph-cu12 --extra-index-url=https://pypi.nvidia.com"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYYN9EpnWphu",
        "outputId": "83eeeb79-4478-4164-e119-3d313f07e125"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://pypi.nvidia.com\n",
            "Requirement already satisfied: nx-cugraph-cu12 in /usr/local/lib/python3.10/dist-packages (24.10.0)\n",
            "Requirement already satisfied: cupy-cuda12x>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from nx-cugraph-cu12) (12.2.0)\n",
            "Requirement already satisfied: networkx>=3.0 in /usr/local/lib/python3.10/dist-packages (from nx-cugraph-cu12) (3.4.1)\n",
            "Requirement already satisfied: numpy<3.0a0,>=1.23 in /usr/local/lib/python3.10/dist-packages (from nx-cugraph-cu12) (1.26.4)\n",
            "Requirement already satisfied: pylibcugraph-cu12==24.10.* in /usr/local/lib/python3.10/dist-packages (from nx-cugraph-cu12) (24.10.0)\n",
            "Requirement already satisfied: nvidia-cublas-cu12 in /usr/local/lib/python3.10/dist-packages (from pylibcugraph-cu12==24.10.*->nx-cugraph-cu12) (12.6.3.3)\n",
            "Requirement already satisfied: nvidia-curand-cu12 in /usr/local/lib/python3.10/dist-packages (from pylibcugraph-cu12==24.10.*->nx-cugraph-cu12) (10.3.7.77)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12 in /usr/local/lib/python3.10/dist-packages (from pylibcugraph-cu12==24.10.*->nx-cugraph-cu12) (11.7.1.2)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12 in /usr/local/lib/python3.10/dist-packages (from pylibcugraph-cu12==24.10.*->nx-cugraph-cu12) (12.5.4.2)\n",
            "Requirement already satisfied: pylibraft-cu12==24.10.* in /usr/local/lib/python3.10/dist-packages (from pylibcugraph-cu12==24.10.*->nx-cugraph-cu12) (24.10.0)\n",
            "Requirement already satisfied: rmm-cu12==24.10.* in /usr/local/lib/python3.10/dist-packages (from pylibcugraph-cu12==24.10.*->nx-cugraph-cu12) (24.10.0)\n",
            "Requirement already satisfied: cuda-python<13.0a0,>=12.0 in /usr/local/lib/python3.10/dist-packages (from pylibraft-cu12==24.10.*->pylibcugraph-cu12==24.10.*->nx-cugraph-cu12) (12.2.1)\n",
            "Requirement already satisfied: numba>=0.57 in /usr/local/lib/python3.10/dist-packages (from rmm-cu12==24.10.*->pylibcugraph-cu12==24.10.*->nx-cugraph-cu12) (0.60.0)\n",
            "Requirement already satisfied: fastrlock>=0.5 in /usr/local/lib/python3.10/dist-packages (from cupy-cuda12x>=12.0.0->nx-cugraph-cu12) (0.8.2)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12->pylibcugraph-cu12==24.10.*->nx-cugraph-cu12) (12.6.77)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.10/dist-packages (from cuda-python<13.0a0,>=12.0->pylibraft-cu12==24.10.*->pylibcugraph-cu12==24.10.*->nx-cugraph-cu12) (3.0.11)\n",
            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.57->rmm-cu12==24.10.*->pylibcugraph-cu12==24.10.*->nx-cugraph-cu12) (0.43.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Of course, we'll also be using `networkx`, which is already provided in the Colab environment. This notebook will be using features added in version 3.4, so we'll import it here to verify we have a compatible version."
      ],
      "metadata": {
        "id": "0h1K-7tI_AZH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YTV0ZTME2tV6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6a7a2e72-3230-4178-d937-90d032332f78"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'3.4.1'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import networkx as nx\n",
        "nx.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's Start with Something Simple\n",
        "\n",
        "To begin, we'll compare NetworkX results without a backend to results of the same algorithm using the `nx-cugraph` backend on a small graph.  `nx.karate_club_graph()` returns an instance of the famous example graph consisting of 34 nodes and 78 edges from Zachary's paper, described [here](https://en.wikipedia.org/wiki/Zachary%27s_karate_club)."
      ],
      "metadata": {
        "id": "UiZKOa3WC7be"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Betweenness Centrality\n",
        "[Betweenness Centrality](https://en.wikipedia.org/wiki/Betweenness_centrality) is a graph algorithm that computes a centrality score for each node (`v`) based on how many of the shortest paths between pairs of nodes in the graph pass through `v`. A higher centrality score represents a node that \"connects\" other nodes in a network more than that of a node with a lower score.\n",
        "\n",
        "First, let's create a NetworkX Graph instance of the the Karate Club graph and inspect it."
      ],
      "metadata": {
        "id": "3atL3tI0frYm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "G = nx.karate_club_graph()\n",
        "G.number_of_nodes(), G.number_of_edges()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSw7EZ46-kRu",
        "outputId": "d22bc140-141f-40d8-d4f5-1aaa345d2a9b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34, 78)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, let's run betweenness centrality and save the results. This should not take long since the Karate Club graph is so small."
      ],
      "metadata": {
        "id": "_-E17u2gKgbC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "nx_bc_results = nx.betweenness_centrality(G)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjxXXKJhKQ4s",
        "outputId": "021290de-d896-498c-d16a-51c394062272"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 3.02 ms, sys: 0 ns, total: 3.02 ms\n",
            "Wall time: 3.03 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's run the same algorithm using the `nx-cugraph` backend.\n",
        "\n",
        "There are several ways to instruct NetworkX to use a particular backend instead of the default implementation. Here we will use the `NX_CUGRAPH_AUTOCONFIG` environment variable, which was added in NetworkX version 3.4.\n",
        "\n",
        "The following line will set the backend to \"cugraph\" and enable graph conversion caching.\n",
        "\n",
        "Some notes:\n",
        "* The standard convention for NetworkX backends is to name the package with a `nx-` prefix to denote they're packages intended to be used with NetworkX, but the `nx-` prefix is not included when referring to them in NetworkX API calls. Here, `nx-cugraph` is the name of the backend package and `\"cugraph\"` is the name NetworkX will use to refer to it.\n",
        "* NetworkX supports the use of multiple backends! If a backend cannot run a particular algorithm (either because it isn't supported, doesn't support a particular option, or for some other reason), NetworkX will fall back to the default implementation.\n",
        "* Many backends have their own data structures for representing the input graph, which are often optimized for that backend's implementation. Prior to running a backend algorithm, NetworkX will have the backend convert the standard NetworkX Graph instance to the backend-specific type. This conversion can be expensive, and rather than repeat it as part of each algorithm call, NetworkX can cache the conversion so it can be skipped on future calls if the graph has not changed. This caching can save significant time and improve overall performance."
      ],
      "metadata": {
        "id": "ClrR3z9XMfLr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%env NX_CUGRAPH_AUTOCONFIG=True"
      ],
      "metadata": {
        "id": "oFHwNqqsNsqS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9abb823e-ef08-45d8-93d4-a5f3deec1655"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: NX_CUGRAPH_AUTOCONFIG=True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "nxcg_bc_results = nx.betweenness_centrality(G)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HrUeWRRQRzFP",
        "outputId": "e3483b16-d3c5-44a9-ebda-54485218eb19"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 3.73 ms, sys: 0 ns, total: 3.73 ms\n",
            "Wall time: 3.74 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You may have noticed that using the `nx-cugraph` backend resulted in a slightly slower execution time. This is not suprising when working with a graph this small, since the overhead of converting the graph for the first time and launching the algorithm kernel on the GPU is actually significantly more than the computation time itself.  We'll see later on that this overhead is negligable when compared to the time we can save by running on the GPU when using large data.\n",
        "\n",
        "Since we've enabled graph conversion caching, we can see that if we re-run the same call the execution time is slightly shorter."
      ],
      "metadata": {
        "id": "z1hxut3GTj5A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "nxcg_bc_results = nx.betweenness_centrality(G)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0c7159d-b75a-4d20-c02d-363ef314f521",
        "id": "7a0XvpUOr9Ju"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2.91 ms, sys: 0 ns, total: 2.91 ms\n",
            "Wall time: 2.92 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Smaller graphs are easy to visualize with NetworkX's plotting utilities. The flexibility of NetworkX's `Graph` instances make it trivial to add the betweenness centrality scores back to the graph object as attributes of the nodes. This will allow us to use those values in the visualization.\n",
        "\n",
        "In this case, we'll create new attributes for each node called \"nx_bc\" for the default NetworkX results, and \"nxcg_bc\" for the nx-cugraph results. We'll use those values to assign the color for each node and plot two graphs side-by-side. This will make it easy to visually validate that the nodes with the higher centrality scores for both implementations match and do indeed appear to be more \"central\" to the other nodes."
      ],
      "metadata": {
        "id": "BzGAphcILFsT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nx.set_node_attributes(G, nx_bc_results, \"nx_bc\")\n",
        "nx.set_node_attributes(G, nxcg_bc_results, \"nxcg_bc\")"
      ],
      "metadata": {
        "id": "1coV6ZfcUoqI"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Configure plot size and layout/position for each node\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams['figure.figsize'] = [12, 8]\n",
        "pos = nx.spring_layout(G)\n",
        "\n",
        "# Assign colors for each set of betweenness centrality results\n",
        "nx_colors = [G.nodes[n][\"nx_bc\"] for n in G.nodes()]\n",
        "nxcg_colors = [G.nodes[n][\"nxcg_bc\"] for n in G.nodes()]\n",
        "\n",
        "# Plot the graph and color each node corresponding to NetworkX betweenness centrality values\n",
        "plt.subplot(1, 2, 1)\n",
        "nx.draw(G, pos=pos, with_labels=True, node_color=nx_colors)\n",
        "\n",
        "# Plot the graph and color each node corresponding to nx-cugraph betweenness centrality values\n",
        "plt.subplot(1, 2, 2)\n",
        "nx.draw(G, pos=pos, with_labels=True, node_color=nxcg_colors)"
      ],
      "metadata": {
        "id": "Sba2iYJgLoN2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "outputId": "dbf0bbb3-4ddd-44b5-bf24-cf7103883ea5"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, the same two nodes (`0` and `33`) are the two most central in both graphs, followed by `2`, `31`, and `32`.\n",
        "\n",
        "## PageRank\n",
        "Another popular graph algorithm is [PageRank](https://en.wikipedia.org/wiki/PageRank). PageRank also assigns scores to each node, but these scores are based on analyzing links to every other node to determine relative \"importance\" within the graph.\n",
        "\n",
        "Let's unset the environment variable so that we're using the default NetworkX implementation and run `nx.pagerank`."
      ],
      "metadata": {
        "id": "dJXH4Zn5VNSg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def unset():\n",
        "  import os\n",
        "\n",
        "  if 'NX_CUGRAPH_AUTOCONFIG' in os.environ:\n",
        "    del os.environ['NX_CUGRAPH_AUTOCONFIG']\n",
        "  else:\n",
        "    print(\"Already unset!\")\n",
        "\n",
        "unset()"
      ],
      "metadata": {
        "id": "9CdYNk62E1v_"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "nx_pr_results = nx.pagerank(G)"
      ],
      "metadata": {
        "id": "Jo39YxVmYolq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e2f31b0-c666-4ff0-a946-312ab2dcab4d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 771 ms, sys: 5.67 ms, total: 777 ms\n",
            "Wall time: 94.6 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We could set `NX_CUGRAPH_AUTOCONFIG` again to use `nx-cugraph` as the backend, but instead, let's show how the `backend` keyword argument can be used to override the priority list and force a particular backend to be used."
      ],
      "metadata": {
        "id": "sV6dM8ToZDiC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "nxcg_pr_results = nx.pagerank(G, backend=\"cugraph\")"
      ],
      "metadata": {
        "id": "oMSvQVGKY0rn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df439ebc-6289-4546-d300-e68ee22602c0"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1.55 s, sys: 382 ms, total: 1.93 s\n",
            "Wall time: 4.48 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Instead of plotting the graph to show that the results are identical, we'll compare them directly using the saved values from both runs."
      ],
      "metadata": {
        "id": "ZGux_8xFZneI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sorted(nx_pr_results) == sorted(nxcg_pr_results)"
      ],
      "metadata": {
        "id": "RcmtdFy4Zw7p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd45d39e-ed4a-47cd-c091-99582ad97f0e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Working with Bigger Data"
      ],
      "metadata": {
        "id": "mefjUEAnZ4pq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we'll look at a larger dataset from https://snap.stanford.edu/data/cit-Patents.html containing citations between U.S. patents granted from January 1, 1963 to December 30, 1999. The dataset represents 16.5M citations (edges) between 3.77M patents (nodes).\n",
        "\n",
        "Data of this size starts to push the limits of the default pure-python NetworkX implementation.\n",
        "\n",
        "Note: _the dataset is available as a compressed file from `snap.stanford.edu` and will require additional arguments to uncompress it and skip headers. The uncompressed data is available without the headers from `data.rapids.ai`. We've included code for both in the notebook below to demonstrate both approaches._"
      ],
      "metadata": {
        "id": "yLY-yl6PuNYo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# The locale encoding may have been modified from the plots above, reset here to run shell commands\n",
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\"\n",
        "!wget https://snap.stanford.edu/data/cit-Patents.txt.gz  # Skip if cit-Patents.txt.gz already exists."
      ],
      "metadata": {
        "id": "lyYF0LbtFwjh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02712649-d947-4e96-ddfb-d7f293b0254f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-10-22 02:40:26--  https://snap.stanford.edu/data/cit-Patents.txt.gz\n",
            "Resolving snap.stanford.edu (snap.stanford.edu)... 171.64.75.80\n",
            "Connecting to snap.stanford.edu (snap.stanford.edu)|171.64.75.80|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 85139832 (81M) [application/x-gzip]\n",
            "Saving to: ‘cit-Patents.txt.gz’\n",
            "\n",
            "cit-Patents.txt.gz  100%[===================>]  81.20M  25.6MB/s    in 3.2s    \n",
            "\n",
            "2024-10-22 02:40:29 (25.6 MB/s) - ‘cit-Patents.txt.gz’ saved [85139832/85139832]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !wget https://data.rapids.ai/cugraph/datasets/cit-Patents.csv  # Skip if cit-Patents.csv already exists.\n",
        "\n",
        "# df = pd.read_csv(\"cit-Patents.csv\",\n",
        "#                 sep=\" \",\n",
        "#                 names=[\"src\", \"dst\"],\n",
        "#                 dtype=\"int32\",\n",
        "# )"
      ],
      "metadata": {
        "id": "JR6_csQzVEuI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are running inside a Colab environment, which will have `cudf` preinstalled. This is a GPU accelerated DataFrame library, and it can be used to speed up the reading of large files. I will call `%load_ext` to enable `cudf.pandas`"
      ],
      "metadata": {
        "id": "eVv_HnFyO1e4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext cudf.pandas\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "kjGINYphQSQ2"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "df = pd.read_csv(\"cit-Patents.txt.gz\",\n",
        "                 compression=\"gzip\",\n",
        "                 skiprows=4,\n",
        "                 sep=\"\\t\",\n",
        "                 names=[\"src\", \"dst\"],\n",
        "                 dtype=\"int32\",\n",
        ")"
      ],
      "metadata": {
        "id": "iV4DieGZOalc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc2bbbf4-f289-4b19-9926-697d577e99e8"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1.31 s, sys: 347 ms, total: 1.66 s\n",
            "Wall time: 1.82 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "G = nx.from_pandas_edgelist(df, source=\"src\", target=\"dst\")\n",
        "G.number_of_nodes(), G.number_of_edges()"
      ],
      "metadata": {
        "id": "PREA67u4eKat",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4605bd4f-1015-4a4c-cbde-2891a7a3747b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 48.9 s, sys: 2.84 s, total: 51.8 s\n",
            "Wall time: 50.4 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3774768, 16518948)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By default, `nx.betweenness_centrality` will perform an all-pairs shortest path analysis when determining the centrality scores for each node. However, due to the large size of this graph, determining the shortest path for all pairs of nodes is not feasible. Instead, we'll use the parameter `k` to limit the number of shortest path computations used for determining the centrality scores (at the expense of accuracy). As we'll see when using a dataset this size with `nx.betweenness_centrality`, we have to limit `k` to `1` which is not practical but is sufficient here for demonstration purposes (since anything larger than `1` will result in many minutes of execution time)."
      ],
      "metadata": {
        "id": "NcsUxBqpu4zY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "bc_results = nx.betweenness_centrality(G, k=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gNDWbj3kAk3j",
        "outputId": "623adaa0-a4b9-4170-ba8e-dfc8365a43e9"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1min 1s, sys: 1.6 s, total: 1min 2s\n",
            "Wall time: 1min 1s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we'll configure NetworkX to use the `nx-cugraph` backend (again, using the naming convention that drops the package name's `nx-` prefix) and run the same call. Because this is a Graph that `nx-cugraph` hasn't seen before, the runtime includes the time it took to internally convert and cache a GPU-based graph."
      ],
      "metadata": {
        "id": "NB8xmxMd1PlX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "bc_results = nx.betweenness_centrality(G, k=1, backend=\"cugraph\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmK8ZuQGvfPo",
        "outputId": "1741eee6-532f-4e00-ebfd-f39ea675740d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/networkx/utils/backends.py:1777: UserWarning: Note: conversions to backend graphs are saved to cache (`G.__networkx_cache__` on the original graph) by default.\n",
            "\n",
            "This warning means the cached graph is being used for the 'cugraph' backend in the call to betweenness_centrality.\n",
            "\n",
            "For the cache to be consistent (i.e., correct), the input graph must not have been manually mutated since the cached graph was created. Examples of manually mutating the graph data structures resulting in an inconsistent cache include:\n",
            "\n",
            "    >>> G[u][v][key] = val\n",
            "\n",
            "and\n",
            "\n",
            "    >>> for u, v, d in G.edges(data=True):\n",
            "    ...     d[key] = val\n",
            "\n",
            "Using methods such as `G.add_edge(u, v, weight=val)` will correctly clear the cache to keep it consistent. You may also use `G.__networkx_cache__.clear()` to manually clear the cache, or set `G.__networkx_cache__` to None to disable caching for G. Enable or disable caching globally via `nx.config.cache_converted_graphs` config.\n",
            "\n",
            "To disable this warning:\n",
            "\n",
            "    >>> nx.config.warnings_to_ignore.add(\"cache\")\n",
            "\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1.05 s, sys: 298 ms, total: 1.35 s\n",
            "Wall time: 1.31 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice the warning above about using the cache. This will only be raised once per graph instance (it can also be easily disabled), but its purpose is to point out that the cache should not be used if the Graph object will have its attribute dictionary modified directly. In this case and many others, we won't be modifying the dictionaries directly. Instead, we will use APIs such as nx.set_node_attributes which properly clear the cache, so it's safe for us to use the cache. Because of that, we'll disable the warning so we don't see it on other graphs in this session."
      ],
      "metadata": {
        "id": "ZosLiIYoFlls"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nx.config.warnings_to_ignore.add(\"cache\")"
      ],
      "metadata": {
        "id": "_Ya0mBVoFklX"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's run betweenness centrality again with a more useful number of samples by setting `k=100`."
      ],
      "metadata": {
        "id": "vdHb1YXP15TZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "bc_results = nx.betweenness_centrality(G, k=100, backend=\"cugraph\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKjIrzL-vrGS",
        "outputId": "2283451a-d087-4fd3-cad9-147ca2de1f7e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 5.42 s, sys: 674 ms, total: 6.09 s\n",
            "Wall time: 5.61 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's also run pagerank on the same dataset to compare."
      ],
      "metadata": {
        "id": "QeMcrAX2HZSM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "nx_pr_results = nx.pagerank(G)"
      ],
      "metadata": {
        "id": "rTFuvX5wb_c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da547061-64bb-4405-c577-d14fa2194bf2"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1min 26s, sys: 4.29 s, total: 1min 30s\n",
            "Wall time: 1min 27s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "nxcg_pr_results = nx.pagerank(G, backend=\"cugraph\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8sJx9aeJV9hv",
        "outputId": "8c0bed88-71de-4b85-a620-cdb83045f4ac"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1.02 s, sys: 288 ms, total: 1.31 s\n",
            "Wall time: 1.28 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted(nx_pr_results) == sorted(nxcg_pr_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGOVQ6ZyY4Ih",
        "outputId": "cda0dbf3-55d0-4f17-a0f0-0648a5ed3b55"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "<i>\n",
        "Each user is responsible for checking the content of datasets and the applicable licenses and determining if suitable for the intended use.\n",
        "<i>\n",
        "<br>\n",
        "<br>\n",
        "<i>\n",
        "Information on the U.S. Patent Citation Network dataset used in this notebook is as follows:\n",
        "<br>Authors: Jure Leskovec and Andrej Krevl\n",
        "<br>Title: SNAP Datasets, Stanford Large Network Dataset Collection\n",
        "<br>URL: http://snap.stanford.edu/data\n",
        "<br>Date: June 2014\n",
        "</i>\n"
      ],
      "metadata": {
        "id": "k2DfAaZaDIBj"
      }
    }
  ]
}