Upload Anonymous_Walk_Embeddings.ipynb

Anonymous_Walk_Embeddings.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2eq2Z1JGYLy7"
+      },
+      "source": [
+        "Assignment 2 : Create ML model based on Anonymous Walk Embeddings for node level prediction"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "- Pramod Manohar Dalavi - 2023aa05398@wilp.bits-pilani.ac.in\n",
+        "- Utkarsh Kumar Verma - 2023ab05014@wilp.bits-pilani.ac.in\n",
+        "- Ankita Laxmikant Bahirat - 2023aa05952@wilp.bits-pilani.ac.in\n",
+        "- Charu Mathur - 2023aa05055@wilp.bits-pilani.ac.in\n",
+        "- K Mamatha - 2023ab05018@wilp.bits-pilani.ac.in"
+      ],
+      "metadata": {
+        "id": "UZPVjT1hwxOF"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QKRg53KHYUGc"
+      },
+      "source": [
+        "Generate Graph Embeddings"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "S0YbS1rMYKQg",
+        "outputId": "57474ad6-f275-424a-e3ca-01cb32fea82e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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+          ]
+        }
+      ],
+      "source": [
+        "import networkx as nx\n",
+        "import numpy as np\n",
+        "\n",
+        "def generate_anonymous_walks(graph, walk_length, num_walks):\n",
+        "    walks = []\n",
+        "    for _ in range(num_walks):\n",
+        "        for node in graph.nodes():\n",
+        "            walk = [node]\n",
+        "            for _ in range(walk_length - 1):\n",
+        "                neighbors = list(graph.neighbors(walk[-1]))\n",
+        "                if neighbors:\n",
+        "                    walk.append(np.random.choice(neighbors))\n",
+        "                else:\n",
+        "                    break\n",
+        "            walks.append(walk)\n",
+        "    return walks\n",
+        "\n",
+        "def anonymous_walk_embedding(graph, walk_length=5, num_walks=10):\n",
+        "    walks = generate_anonymous_walks(graph, walk_length, num_walks)\n",
+        "    # Convert walks to anonymous walks\n",
+        "    anon_walks = []\n",
+        "    for walk in walks:\n",
+        "        anon_walk = []\n",
+        "        mapping = {}\n",
+        "        next_id = 0\n",
+        "        for node in walk:\n",
+        "            if node not in mapping:\n",
+        "                mapping[node] = next_id\n",
+        "                next_id += 1\n",
+        "            anon_walk.append(mapping[node])\n",
+        "        anon_walks.append(anon_walk)\n",
+        "    return anon_walks\n",
+        "\n",
+        "# Example usage\n",
+        "G = nx.karate_club_graph()\n",
+        "embeddings = anonymous_walk_embedding(G)\n",
+        "print(embeddings)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fgMkAPGpYX7A"
+      },
+      "source": [
+        "Dataset Preparation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4eiZ0-p0Y_HY",
+        "outputId": "573d7c65-b94d-4168-90c8-48429fa837e6"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting ogb\n",
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+            "\u001b[?25hDownloading outdated-0.2.2-py2.py3-none-any.whl (7.5 kB)\n",
+            "Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m62.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m48.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m35.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m83.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading littleutils-0.2.4-py3-none-any.whl (8.1 kB)\n",
+            "Installing collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, littleutils, outdated, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, ogb\n",
+            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
+            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
+            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-curand-cu12\n",
+            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
+            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
+            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
+            "  Attempting uninstall: nvidia-cufft-cu12\n",
+            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
+            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
+            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
+            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
+            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
+            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cublas-cu12\n",
+            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
+            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
+            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
+            "  Attempting uninstall: nvidia-cusparse-cu12\n",
+            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
+            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
+            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
+            "  Attempting uninstall: nvidia-cudnn-cu12\n",
+            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
+            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
+            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
+            "  Attempting uninstall: nvidia-cusolver-cu12\n",
+            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
+            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
+            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
+            "Successfully installed littleutils-0.2.4 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 ogb-1.3.6 outdated-0.2.2\n",
+            "Requirement already satisfied: ogb in /usr/local/lib/python3.11/dist-packages (1.3.6)\n",
+            "Collecting torch-geometric\n",
+            "  Downloading torch_geometric-2.6.1-py3-none-any.whl.metadata (63 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.1/63.1 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from ogb) (2.5.1+cu124)\n",
+            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.11/dist-packages (from ogb) (1.26.4)\n",
+            "Requirement already satisfied: tqdm>=4.29.0 in /usr/local/lib/python3.11/dist-packages (from ogb) (4.67.1)\n",
+            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.11/dist-packages (from ogb) (1.6.1)\n",
+            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.11/dist-packages (from ogb) (2.2.2)\n",
+            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.11/dist-packages (from ogb) (1.17.0)\n",
+            "Requirement already satisfied: urllib3>=1.24.0 in /usr/local/lib/python3.11/dist-packages (from ogb) (2.3.0)\n",
+            "Requirement already satisfied: outdated>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from ogb) (0.2.2)\n",
+            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from torch-geometric) (3.11.13)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch-geometric) (2024.10.0)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch-geometric) (3.1.6)\n",
+            "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.11/dist-packages (from torch-geometric) (5.9.5)\n",
+            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.11/dist-packages (from torch-geometric) (3.2.1)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from torch-geometric) (2.32.3)\n",
+            "Requirement already satisfied: setuptools>=44 in /usr/local/lib/python3.11/dist-packages (from outdated>=0.2.0->ogb) (75.1.0)\n",
+            "Requirement already satisfied: littleutils in /usr/local/lib/python3.11/dist-packages (from outdated>=0.2.0->ogb) (0.2.4)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24.0->ogb) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24.0->ogb) (2025.1)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=0.24.0->ogb) (2025.1)\n",
+            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=0.20.0->ogb) (1.14.1)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=0.20.0->ogb) (1.4.2)\n",
+            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=0.20.0->ogb) (3.5.0)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (3.17.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (4.12.2)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (3.4.2)\n",
+            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (12.4.127)\n",
+            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (12.4.127)\n",
+            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (12.4.127)\n",
+            "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (9.1.0.70)\n",
+            "Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (12.4.5.8)\n",
+            "Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (11.2.1.3)\n",
+            "Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (10.3.5.147)\n",
+            "Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (11.6.1.9)\n",
+            "Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (12.3.1.170)\n",
+            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (2.21.5)\n",
+            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (12.4.127)\n",
+            "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (12.4.127)\n",
+            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (3.1.0)\n",
+            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.6.0->ogb) (1.13.1)\n",
+            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.6.0->ogb) (1.3.0)\n",
+            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->torch-geometric) (2.5.0)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp->torch-geometric) (1.3.2)\n",
+            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->torch-geometric) (25.1.0)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp->torch-geometric) (1.5.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp->torch-geometric) (6.1.0)\n",
+            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->torch-geometric) (0.3.0)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->torch-geometric) (1.18.3)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch-geometric) (3.0.2)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->torch-geometric) (3.4.1)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->torch-geometric) (3.10)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->torch-geometric) (2025.1.31)\n",
+            "Downloading torch_geometric-2.6.1-py3-none-any.whl (1.1 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: torch-geometric\n",
+            "Successfully installed torch-geometric-2.6.1\n",
+            "1.3.6\n",
+            "2.6.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install ogb\n",
+        "!pip install ogb torch-geometric\n",
+        "import ogb\n",
+        "import torch_geometric\n",
+        "\n",
+        "print(ogb.__version__)\n",
+        "print(torch_geometric.__version__)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4B8MF6xVZ6qa",
+        "outputId": "30a2c8a5-15f6-4304-a8ec-2bacddefb7a2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1.3.6\n",
+            "2.6.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "import ogb\n",
+        "import torch_geometric\n",
+        "\n",
+        "print(ogb.__version__)\n",
+        "print(torch_geometric.__version__)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mAQtd8gjaBb0",
+        "outputId": "e948110b-f189-4696-bf8a-437ec7ddab3e"
+      },
+      "outputs": [
+        {
+          "metadata": {
+            "tags": null
+          },
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Found existing installation: ogb 1.3.6\n",
+            "Uninstalling ogb-1.3.6:\n",
+            "  Would remove:\n",
+            "    /usr/local/lib/python3.11/dist-packages/ogb-1.3.6.dist-info/*\n",
+            "    /usr/local/lib/python3.11/dist-packages/ogb/*\n",
+            "Proceed (Y/n)? "
+          ]
+        }
+      ],
+      "source": [
+        "#!pip uninstall ogb torch-geometric\n",
+        "#!pip install ogb torch-geometric"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZKT-dMUbYZj1"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "from ogb.graphproppred import PygGraphPropPredDataset\n",
+        "from torch_geometric.data import DataLoader\n",
+        "\n",
+        "# Load dataset\n",
+        "dataset = PygGraphPropPredDataset(name=\"ogbg-molhiv\")\n",
+        "split_idx = dataset.get_idx_split()\n",
+        "train_loader = DataLoader(dataset[split_idx[\"train\"]], batch_size=32, shuffle=True)\n",
+        "valid_loader = DataLoader(dataset[split_idx[\"valid\"]], batch_size=32, shuffle=False)\n",
+        "test_loader = DataLoader(dataset[split_idx[\"test\"]], batch_size=32, shuffle=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pdNjBmBJYcOP"
+      },
+      "source": [
+        "Neural Network Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5tTY5vQ3Yd3y"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "from torch_geometric.nn import GCNConv, global_mean_pool\n",
+        "\n",
+        "class GNN(torch.nn.Module):\n",
+        "    def __init__(self, hidden_channels):\n",
+        "        super(GNN, self).__init__()  # Call the parent class's __init__ method\n",
+        "        self.conv1 = GCNConv(dataset.num_node_features, hidden_channels)\n",
+        "        self.conv2 = GCNConv(hidden_channels, hidden_channels)\n",
+        "        self.lin = torch.nn.Linear(hidden_channels, dataset.num_tasks)\n",
+        "\n",
+        "    def forward(self, x, edge_index, batch):\n",
+        "        x = self.conv1(x, edge_index).relu()\n",
+        "        x = self.conv2(x, edge_index).relu()\n",
+        "        x = global_mean_pool(x, batch)  # Global pooling\n",
+        "        x = self.lin(x)\n",
+        "        return x\n",
+        "\n",
+        "model = GNN(hidden_channels=64)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ot4ypsx1YgBq"
+      },
+      "source": [
+        "Model Optimization"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ldHLgvdJYh86"
+      },
+      "outputs": [],
+      "source": [
+        "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
+        "criterion = torch.nn.BCEWithLogitsLoss()\n",
+        "\n",
+        "def train():\n",
+        "    model.train()\n",
+        "    for data in train_loader:\n",
+        "        optimizer.zero_grad()\n",
+        "        out = model(data.x, data.edge_index, data.batch)\n",
+        "        loss = criterion(out, data.y.float())\n",
+        "        loss.backward()\n",
+        "        optimizer.step()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9MWIVvcRYlpB"
+      },
+      "source": [
+        "Evaluation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ui9ln9PlbzZD"
+      },
+      "outputs": [],
+      "source": [
+        "# Inspect the first element of the dataset\n",
+        "sample_data = dataset[0]\n",
+        "print(sample_data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true
+        },
+        "id": "J-gzaiejcpvx"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "from ogb.graphproppred import GraphPropPredDataset, Evaluator\n",
+        "from torch_geometric.data import DataLoader, Data\n",
+        "from torch_geometric.nn import GCNConv, global_mean_pool\n",
+        "\n",
+        "# Load dataset\n",
+        "dataset = GraphPropPredDataset(name=\"ogbg-molhiv\")\n",
+        "split_idx = dataset.get_idx_split()\n",
+        "\n",
+        "# Convert split indices to lists of integers\n",
+        "train_idx = split_idx[\"train\"].tolist()\n",
+        "valid_idx = split_idx[\"valid\"].tolist()\n",
+        "test_idx = split_idx[\"test\"].tolist()\n",
+        "\n",
+        "# Convert dataset to PyTorch Geometric Data objects\n",
+        "def convert_to_pyg_data(data_dict, label):\n",
+        "    return Data(\n",
+        "        x=torch.tensor(data_dict['node_feat'], dtype=torch.float),\n",
+        "        edge_index=torch.tensor(data_dict['edge_index'], dtype=torch.long),\n",
+        "        edge_attr=torch.tensor(data_dict['edge_feat'], dtype=torch.float),\n",
+        "        y=torch.tensor(label, dtype=torch.float).view(-1, 1)  # Reshape the label\n",
+        "    )\n",
+        "\n",
+        "train_data = [convert_to_pyg_data(dataset[i][0], dataset[i][1]) for i in train_idx]\n",
+        "valid_data = [convert_to_pyg_data(dataset[i][0], dataset[i][1]) for i in valid_idx]\n",
+        "test_data = [convert_to_pyg_data(dataset[i][0], dataset[i][1]) for i in test_idx]\n",
+        "\n",
+        "train_loader = DataLoader(train_data, batch_size=32, shuffle=True)\n",
+        "valid_loader = DataLoader(valid_data, batch_size=32, shuffle=False)\n",
+        "test_loader = DataLoader(test_data, batch_size=32, shuffle=False)\n",
+        "\n",
+        "# Determine the number of node features\n",
+        "num_node_features = train_data[0].x.shape[1]\n",
+        "num_tasks = dataset.num_tasks\n",
+        "\n",
+        "class GNN(torch.nn.Module):\n",
+        "    def __init__(self, hidden_channels):\n",
+        "        super(GNN, self).__init__()\n",
+        "        self.conv1 = GCNConv(num_node_features, hidden_channels)\n",
+        "        self.conv2 = GCNConv(hidden_channels, hidden_channels)\n",
+        "        self.lin = torch.nn.Linear(hidden_channels, num_tasks)\n",
+        "\n",
+        "    def forward(self, x, edge_index, batch):\n",
+        "        x = self.conv1(x, edge_index).relu()\n",
+        "        x = self.conv2(x, edge_index).relu()\n",
+        "        x = global_mean_pool(x, batch)  # Global pooling\n",
+        "        x = self.lin(x)\n",
+        "        return x\n",
+        "\n",
+        "model = GNN(hidden_channels=64)\n",
+        "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
+        "criterion = torch.nn.BCEWithLogitsLoss()\n",
+        "\n",
+        "# Training loop\n",
+        "def train():\n",
+        "    model.train()\n",
+        "    for data in train_loader:\n",
+        "        optimizer.zero_grad()\n",
+        "        out = model(data.x, data.edge_index, data.batch)\n",
+        "        loss = criterion(out, data.y)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "# Evaluation\n",
+        "def evaluate(loader):\n",
+        "    model.eval()\n",
+        "    y_true = []\n",
+        "    y_pred = []\n",
+        "    for data in loader:\n",
+        "        with torch.no_grad():\n",
+        "            out = model(data.x, data.edge_index, data.batch)\n",
+        "        y_true.append(data.y.view(-1, 1).cpu())\n",
+        "        y_pred.append(out.view(-1, 1).cpu())\n",
+        "    y_true = torch.cat(y_true, dim=0).numpy()\n",
+        "    y_pred = torch.cat(y_pred, dim=0).numpy()\n",
+        "    return evaluator.eval({\"y_true\": y_true, \"y_pred\": y_pred})[\"rocauc\"]\n",
+        "\n",
+        "evaluator = Evaluator(name=\"ogbg-molhiv\")\n",
+        "for epoch in range(1, 101):\n",
+        "    train()\n",
+        "    valid_rocauc = evaluate(valid_loader)\n",
+        "    print(f'Epoch: {epoch:03d}, Validation ROC-AUC: {valid_rocauc:.4f}')"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}