04_15_GraphML_Lecture03.ipynb

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        "<a href=\"https://colab.research.google.com/github/ua-datalab/GraphML/blob/main/04_15_GraphML_Lecture03.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# **Lecture 3: Node classification with vanilla neural networks and vanilla graph neural networks**"
      ],
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        "### Graph datasets often extend beyond a simple collection of connections between entities. In addition to the nodes and edges that form the core of the graph structure, these datasets can also include a wealth of supplementary information. Nodes and edges may be associated with various features, such as scores, colors, textual attributes, and other descriptive properties.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### Incorporating this auxiliary data into the input for graph-based machine learning models is crucial for generating high-quality embeddings and representations. By leveraging both the structural information and the rich node and edge features, the models can learn more comprehensive and meaningful representations of the underlying entities and their relationships.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### This concept of blending structured graph data with additional feature information is a natural extension of traditional machine learning techniques. Just as tabular datasets in non-graph settings often include multiple columns of descriptive attributes, graph datasets harness the power of both the relational structure and the node- and edge-level features to enable more insightful and accurate learning."
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        "# Introducing graph datasets:\n",
        "\n",
        "\n",
        "\n",
        "*  **CORA:** It consists of a citation network of 2,708 scientific publications, with each publication belonging to one of seven classes, such as Neural Networks, Reinforcement Learning, or Rule Learning. The dataset captures the citation relationships between these publications, represented by 5,429 directed edges. This structured data allows researchers to evaluate how well graph neural network (GNN) models can leverage both the content information (e.g., the text of the publication abstracts) and the relational structure (the citation links) to accurately classify the publications into their respective topics.\n",
        "*   **Facebook Page-Page Dataset:** It is another popular benchmark for evaluating graph neural network models. It represents the relationships between 22,470 Facebook pages, which are categorized into four classes: Politician, Government, Company, and Public Figure. The dataset contains 171,002 connections (edges) between these pages, capturing the complex social interactions and affinities between different types of entities on the Facebook platform.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "## **The Cora Dataset**"
      ],
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        "id": "dmGrDCQPoPKB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://graphsandnetworks.com/wp-content/uploads/2019/09/CoraBalloons.png\" alt=\"Description of Image\" width=\"500\" height=\"auto\">\n"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "## **The Facebook Page-Page Dataset**"
      ],
      "metadata": {
        "id": "l9XjsfR6oP7W"
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      "cell_type": "markdown",
      "source": [
        "![Screenshot from 2024-04-14 15-04-28.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "3d8Fb_5IoQAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## These datasets are richer than the zachary karate club graph we discussed.\n",
        "\n",
        "| Graph             | Nodes    | Edges |Classes |\n",
        "| :---------------: | :------: | :---: | :---:  |\n",
        "| Zachary Karate Club|   34   | 78  |    2  (not predefined) |\n",
        "| Cora          |   2,708   | 5,429 |       7 |\n",
        "| FB Page-Page    |  22,470   | 171,002 |     4   |\n"
      ],
      "metadata": {
        "id": "_-pKylvvEg21"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Installing pytorch-geometric**"
      ],
      "metadata": {
        "id": "WmXeEcSZEg-t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch_geometric"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZWadcHsXX6q",
        "outputId": "0e2d62f8-252a-423e-ca53-da6b93002249"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torch_geometric\n",
            "  Downloading torch_geometric-2.5.2-py3-none-any.whl (1.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (4.66.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (1.11.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (2023.6.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (3.1.3)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (3.9.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (2.31.0)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (3.1.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (1.2.2)\n",
            "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (5.9.5)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (4.0.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch_geometric) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (2024.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch_geometric) (1.4.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch_geometric) (3.4.0)\n",
            "Installing collected packages: torch_geometric\n",
            "Successfully installed torch_geometric-2.5.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Load datasets**"
      ],
      "metadata": {
        "id": "JOGh19klm5EV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch_geometric.datasets import Planetoid, FacebookPagePage"
      ],
      "metadata": {
        "id": "rooFpmkoXLby"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_CORA = Planetoid(root=\".\", name=\"Cora\")"
      ],
      "metadata": {
        "id": "kx9bQxJiXLf7"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_graph_CORA = dataset_CORA[0] # get the cora graph"
      ],
      "metadata": {
        "id": "uguLAmDDmqua"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print information about the dataset\n",
        "print(f'Dataset: {dataset_CORA}')\n",
        "print('---------------')\n",
        "print(f'Number of graphs: {len(dataset_CORA)}')\n",
        "print(f'Number of nodes: {data_graph_CORA.x.shape[0]}')\n",
        "print(f'Number of features: {dataset_CORA.num_features}')\n",
        "print(f'Number of classes: {dataset_CORA.num_classes}')\n",
        "\n",
        "# Print information about the graph\n",
        "print(f'\\nGraph:')\n",
        "print('------')\n",
        "print(f'Edges are directed: {data_graph_CORA.is_directed()}')\n",
        "print(f'Graph has isolated nodes: {data_graph_CORA.has_isolated_nodes()}')\n",
        "print(f'Graph has loops: {data_graph_CORA.has_self_loops()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iq3nhsb8mqyu",
        "outputId": "760d454c-b554-475b-b16f-08f8ae3754b5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset: Cora()\n",
            "---------------\n",
            "Number of graphs: 1\n",
            "Number of nodes: 2708\n",
            "Number of features: 1433\n",
            "Number of classes: 7\n",
            "\n",
            "Graph:\n",
            "------\n",
            "Edges are directed: False\n",
            "Graph has isolated nodes: False\n",
            "Graph has loops: False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_FBPP = FacebookPagePage(root=\"./facebook_pagepage\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xLZWSbwOmq3k",
        "outputId": "883f3fea-8a00-4b77-99de-fd4f4c554211"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://graphmining.ai/datasets/ptg/facebook.npz\n",
            "Processing...\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_graph_FBPP = dataset_FBPP[0] # the graph"
      ],
      "metadata": {
        "id": "QHBnAE54mq65"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print information about the dataset\n",
        "print(f'Dataset: {dataset_FBPP}')\n",
        "print('---------------')\n",
        "print(f'Number of graphs: {len(dataset_FBPP)}')\n",
        "print(f'Number of nodes: {data_graph_FBPP.x.shape[0]}')\n",
        "print(f'Number of features: {dataset_FBPP.num_features}')\n",
        "print(f'Number of classes: {dataset_FBPP.num_classes}')\n",
        "\n",
        "# Print information about the graph\n",
        "print(f'\\nGraph:')\n",
        "print('------')\n",
        "print(f'Edges are directed: {data_graph_FBPP.is_directed()}')\n",
        "print(f'Graph has isolated nodes: {data_graph_FBPP.has_isolated_nodes()}')\n",
        "print(f'Graph has loops: {data_graph_FBPP.has_self_loops()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbR6ozcNmq8z",
        "outputId": "d2094db6-2f17-49b4-efbb-056bbec505af"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset: FacebookPagePage()\n",
            "---------------\n",
            "Number of graphs: 1\n",
            "Number of nodes: 22470\n",
            "Number of features: 128\n",
            "Number of classes: 4\n",
            "\n",
            "Graph:\n",
            "------\n",
            "Edges are directed: False\n",
            "Graph has isolated nodes: False\n",
            "Graph has loops: True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Facebook Page-Page dataset does not have training, evaluation, test by default so we make them"
      ],
      "metadata": {
        "id": "bMDIOSGXoJ5o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_FBPP.data.train_mask = range(18000)\n",
        "dataset_FBPP.data.val_mask = range(18001, 20000)\n",
        "dataset_FBPP.data.test_mask = range(20001, 22470)"
      ],
      "metadata": {
        "id": "0NxpIrt7mq97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b93c9bee-d9d5-42ce-ab62-ae491e88c120"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch_geometric/data/in_memory_dataset.py:300: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "yRzb-3cqo2Ro"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Now, coming back to Cora dataset and we will train a simple multi-layer perceptron (Vanilla Neural Network )"
      ],
      "metadata": {
        "id": "u3CHEYXronNO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MLP on Cora"
      ],
      "metadata": {
        "id": "0fb4AKGvoiP9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df_x = pd.DataFrame(data_graph_CORA.x.numpy())\n",
        "\n",
        "df_x['label'] = pd.DataFrame(data_graph_CORA.y)"
      ],
      "metadata": {
        "id": "vEoUuUskmrAp"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "eDxAai94pBdQ",
        "outputId": "afe71a9f-301a-46e3-8faf-7bf85536a201"
      },
      "execution_count": 12,
      "outputs": [
        {
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_x"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "torch.manual_seed(0)\n",
        "from torch.nn import Linear\n",
        "import torch.nn.functional as F"
      ],
      "metadata": {
        "id": "2HrUHoSlpBj5"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def accuracy(y_pred, y_true):\n",
        "    return torch.sum(y_pred == y_true) / len(y_true)"
      ],
      "metadata": {
        "id": "f7EN4dKppBoW"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MLP(torch.nn.Module):\n",
        "\n",
        "    def __init__(self, dim_in, dim_h, dim_out):\n",
        "        super().__init__()\n",
        "        self.linear1 = Linear(dim_in, dim_h)\n",
        "        self.linear2 = Linear(dim_h, dim_out)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.linear1(x)\n",
        "        x = torch.relu(x)\n",
        "        x = self.linear2(x)\n",
        "\n",
        "        return F.log_softmax(x, dim=1)\n",
        "\n",
        "    def fit(self, data, epochs):\n",
        "        criterion = torch.nn.CrossEntropyLoss()\n",
        "        optimizer = torch.optim.Adam(self.parameters(), lr=0.01, weight_decay=5e-4)\n",
        "        self.train()\n",
        "\n",
        "        for epoch in range(epochs+1):\n",
        "            optimizer.zero_grad()\n",
        "            out = self(data.x)\n",
        "            loss = criterion(out[data.train_mask], data.y[data.train_mask])\n",
        "            acc = accuracy(out[data.train_mask].argmax(dim=1),\n",
        "                          data.y[data.train_mask])\n",
        "\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            if epoch % 20 ==0:\n",
        "                val_loss = criterion(out[data.val_mask], data.y[data.val_mask])\n",
        "                val_acc = accuracy(out[data.val_mask].argmax(dim=1), data.y[data.val_mask])\n",
        "                print(f'Epoch {epoch:>3} | Train Loss: {loss:.3f} | Train Acc:'\n",
        "                      f' {acc*100:>5.2f}% | Val Loss: {val_loss:.2f} | '\n",
        "                      f'Val Acc: {val_acc*100:.2f}%')\n",
        "\n",
        "    @torch.no_grad()\n",
        "    def test(self, data):\n",
        "        self.eval()\n",
        "        out = self(data.x)\n",
        "        acc = accuracy(out.argmax(dim=1)[data.test_mask], data.y[data.test_mask])\n",
        "        return acc"
      ],
      "metadata": {
        "id": "4F1k8qvXpBp9"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlp = MLP(dataset_CORA.num_features, 16, dataset_CORA.num_classes)"
      ],
      "metadata": {
        "id": "YE35jNw-pBry"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZ492IgnpBuZ",
        "outputId": "52eb53a2-3200-455b-dba2-f16b4d58b102"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLP(\n",
              "  (linear1): Linear(in_features=1433, out_features=16, bias=True)\n",
              "  (linear2): Linear(in_features=16, out_features=7, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlp.fit(dataset_CORA.data, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvNHRyfIpBwu",
        "outputId": "3cdb7364-a131-4ada-da93-80832be87798"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch_geometric/data/in_memory_dataset.py:300: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch   0 | Train Loss: 1.959 | Train Acc: 14.29% | Val Loss: 2.00 | Val Acc: 12.40%\n",
            "Epoch  20 | Train Loss: 0.110 | Train Acc: 100.00% | Val Loss: 1.46 | Val Acc: 49.40%\n",
            "Epoch  40 | Train Loss: 0.014 | Train Acc: 100.00% | Val Loss: 1.44 | Val Acc: 51.00%\n",
            "Epoch  60 | Train Loss: 0.008 | Train Acc: 100.00% | Val Loss: 1.40 | Val Acc: 53.80%\n",
            "Epoch  80 | Train Loss: 0.008 | Train Acc: 100.00% | Val Loss: 1.37 | Val Acc: 55.40%\n",
            "Epoch 100 | Train Loss: 0.009 | Train Acc: 100.00% | Val Loss: 1.34 | Val Acc: 54.60%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = mlp.test(dataset_CORA.data)"
      ],
      "metadata": {
        "id": "nlOFrbjapBzY"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\nMLP on CORA test accuracy: {acc*100:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QFSxxPcNpB1o",
        "outputId": "17cbde95-3a42-459b-f460-cb0c2ea12e84"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "MLP test accuracy: 53.40%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **MLP on Facebook Page-Page Dataset**"
      ],
      "metadata": {
        "id": "DpvA6If3rTta"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mlp_fb = MLP(dataset_FBPP.num_features, 16, dataset_FBPP.num_classes)"
      ],
      "metadata": {
        "id": "nU8ftBy6pB4A"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlp_fb.fit(dataset_FBPP.data, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mDAkWNSpB6z",
        "outputId": "5cb6044c-3564-44b3-d971-75c8ed464b79"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch   0 | Train Loss: 1.406 | Train Acc: 21.38% | Val Loss: 1.41 | Val Acc: 21.61%\n",
            "Epoch  20 | Train Loss: 0.659 | Train Acc: 73.50% | Val Loss: 0.67 | Val Acc: 72.04%\n",
            "Epoch  40 | Train Loss: 0.578 | Train Acc: 76.80% | Val Loss: 0.62 | Val Acc: 74.79%\n",
            "Epoch  60 | Train Loss: 0.548 | Train Acc: 78.05% | Val Loss: 0.61 | Val Acc: 75.09%\n",
            "Epoch  80 | Train Loss: 0.531 | Train Acc: 78.63% | Val Loss: 0.60 | Val Acc: 75.34%\n",
            "Epoch 100 | Train Loss: 0.518 | Train Acc: 79.19% | Val Loss: 0.61 | Val Acc: 75.29%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = mlp_fb.test(dataset_FBPP.data)\n",
        "print(f'\\nMLP on Facebook Page-Page test accuracy: {acc*100:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p98AN52HpB9P",
        "outputId": "653d84e8-c43e-4c75-ea66-5cfd7bd95b6b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "MLP test accuracy: 75.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Coding our first Vanilla Graph neural network**"
      ],
      "metadata": {
        "id": "YmVOeLuHrxkg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Recap - Understanding Node Features and the Adjacency Matrix\n",
        "\n",
        "- **Node Features**: In a graph, each node can have attributes or features represented as a vector. For instance, in a social network, features could include age, location, and interests for each user. These features are typically organized into a matrix \\(X\\) where each row represents a node and each column represents a feature.\n",
        "\n",
        "- **Adjacency Matrix**: This is a square matrix used to represent a graph, where the element at row \\(i\\) and column \\(j\\) indicates whether there is an edge from node \\(i\\) to node \\(j\\). In simple terms, it shows how nodes are connected. For undirected graphs, this matrix is symmetric.\n",
        "\n",
        "## Multiplication of Adjacency Matrix with Node Features\n",
        "\n",
        "- **Operation**: When you multiply the adjacency matrix \\(A\\) with the node features matrix \\(X\\) (i.e., \\(AX\\)), each entry in the resulting matrix is computed as a weighted sum of the features of adjacent nodes. Specifically, the element at row \\(i\\) in the result represents the combined features of all nodes connected to node \\(i\\), weighted by their respective connection strengths (if the edges have weights).\n",
        "\n",
        "## Integrating Local Neighborhood Information\n",
        "\n",
        "- **Aggregation**: The multiplication \\(AX\\) aggregates information across the immediate neighbors of each node. This means that the feature vector of each node in the output matrix is now a blend of its own features and those of its neighbors. This aggregation is fundamental in GNNs because it allows the model to learn from not just the features of a single node but from its local neighborhood structure as well.\n",
        "\n",
        "## Why This Makes It a GNN\n",
        "\n",
        "- **Learning from Graph Structure**: By repeatedly applying this multiplication through multiple layers (with non-linear transformations and possibly other operations in between), a GNN can effectively learn to represent and classify nodes based on both their features and the broader structure of the graph. This is crucial in tasks where the connectivity of the data points (nodes) impacts the output, such as predicting protein interactions in a biological network or recommending friends in a social network.\n",
        "\n",
        "- **Feature Propagation**: The process of multiplying by the adjacency matrix essentially propagates and updates node features across the graph. This enables the GNN to capture patterns at various levels of the graph's topology, from immediate neighbors to larger subgraphs, depending on the depth of the network."
      ],
      "metadata": {
        "id": "37jY1SX6fPcm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class VanillaGNNLayer(torch.nn.Module):\n",
        "    def __init__(self, dim_in, dim_out):\n",
        "        super().__init__()\n",
        "        self.linear = Linear(dim_in, dim_out, bias=False)\n",
        "\n",
        "    def forward(self, x, adjacency):\n",
        "        x = self.linear(x)\n",
        "        x = torch.sparse.mm(adjacency, x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "DsXLffEMpCAF"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch_geometric.utils import to_dense_adj\n",
        "\n",
        "adjacency_cora = to_dense_adj(dataset_CORA.data.edge_index)[0]\n",
        "adjacency_cora += torch.eye(len(adjacency_cora))\n",
        "adjacency_cora\n",
        "\n",
        "\n",
        "\n",
        "adjacency_fbpp = to_dense_adj(dataset_FBPP.data.edge_index)[0]\n",
        "adjacency_fbpp += torch.eye(len(adjacency_fbpp))\n",
        "adjacency_fbpp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tqKTieNupCCn",
        "outputId": "1c3f0bf8-630f-4f01-c049-b696440f94f4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1., 0., 0.,  ..., 0., 0., 0.],\n",
              "        [0., 1., 0.,  ..., 0., 0., 0.],\n",
              "        [0., 0., 1.,  ..., 0., 0., 0.],\n",
              "        ...,\n",
              "        [0., 0., 0.,  ..., 1., 0., 0.],\n",
              "        [0., 0., 0.,  ..., 0., 1., 0.],\n",
              "        [0., 0., 0.,  ..., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adjacency_cora.shape, adjacency_fbpp.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DM-FLwUYtSMs",
        "outputId": "05ef0ad8-e363-4586-abe7-1aed3e79eefb"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([2708, 2708]), torch.Size([22470, 22470]))"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class VanillaGNN(torch.nn.Module):\n",
        "    \"\"\"Vanilla Graph Neural Network\"\"\"\n",
        "    def __init__(self, dim_in, dim_h, dim_out):\n",
        "        super().__init__()\n",
        "        self.gnn1 = VanillaGNNLayer(dim_in, dim_h)\n",
        "        self.gnn2 = VanillaGNNLayer(dim_h, dim_out)\n",
        "\n",
        "    def forward(self, x, adjacency):\n",
        "        h = self.gnn1(x, adjacency)\n",
        "        h = torch.relu(h)\n",
        "        h = self.gnn2(h, adjacency)\n",
        "        return F.log_softmax(h, dim=1)\n",
        "\n",
        "    def fit(self, data, epochs):\n",
        "        criterion = torch.nn.CrossEntropyLoss()\n",
        "        optimizer = torch.optim.Adam(self.parameters(),\n",
        "                                      lr=0.01,\n",
        "                                      weight_decay=5e-4)\n",
        "\n",
        "        self.train()\n",
        "        for epoch in range(epochs+1):\n",
        "            optimizer.zero_grad()\n",
        "            out = self(data.x, adjacency)\n",
        "            loss = criterion(out[data.train_mask], data.y[data.train_mask])\n",
        "            acc = accuracy(out[data.train_mask].argmax(dim=1),\n",
        "                          data.y[data.train_mask])\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            if(epoch % 20 == 0):\n",
        "                val_loss = criterion(out[data.val_mask], data.y[data.val_mask])\n",
        "                val_acc = accuracy(out[data.val_mask].argmax(dim=1),\n",
        "                                  data.y[data.val_mask])\n",
        "                print(f'Epoch {epoch:>3} | Train Loss: {loss:.3f} | Train Acc:'\n",
        "                      f' {acc*100:>5.2f}% | Val Loss: {val_loss:.2f} | '\n",
        "                      f'Val Acc: {val_acc*100:.2f}%')\n",
        "\n",
        "    @torch.no_grad()\n",
        "    def test(self, data):\n",
        "        self.eval()\n",
        "        out = self(data.x, adjacency)\n",
        "        acc = accuracy(out.argmax(dim=1)[data.test_mask], data.y[data.test_mask])\n",
        "        return acc"
      ],
      "metadata": {
        "id": "qezQbWahtSRM"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the Vanilla GNN model\n",
        "\n",
        "adjacency = adjacency_cora\n",
        "gnn = VanillaGNN(dataset_CORA.num_features, 16, dataset_CORA.num_classes)\n",
        "print(gnn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjlPpkDDtSTf",
        "outputId": "bad3c7ad-d4cd-4e4b-bd1d-35cb70cb747a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VanillaGNN(\n",
            "  (gnn1): VanillaGNNLayer(\n",
            "    (linear): Linear(in_features=1433, out_features=16, bias=False)\n",
            "  )\n",
            "  (gnn2): VanillaGNNLayer(\n",
            "    (linear): Linear(in_features=16, out_features=7, bias=False)\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train\n",
        "gnn.fit(dataset_CORA.data, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JHUC7NzmtSVq",
        "outputId": "e3da630a-ef85-42d8-bbe5-ffd9d4e7f157"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch   0 | Train Loss: 2.351 | Train Acc: 20.00% | Val Loss: 2.37 | Val Acc: 9.00%\n",
            "Epoch  20 | Train Loss: 0.101 | Train Acc: 97.86% | Val Loss: 1.62 | Val Acc: 73.60%\n",
            "Epoch  40 | Train Loss: 0.013 | Train Acc: 100.00% | Val Loss: 2.20 | Val Acc: 72.40%\n",
            "Epoch  60 | Train Loss: 0.005 | Train Acc: 100.00% | Val Loss: 2.39 | Val Acc: 72.00%\n",
            "Epoch  80 | Train Loss: 0.003 | Train Acc: 100.00% | Val Loss: 2.40 | Val Acc: 72.60%\n",
            "Epoch 100 | Train Loss: 0.002 | Train Acc: 100.00% | Val Loss: 2.39 | Val Acc: 73.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test\n",
        "acc = gnn.test(dataset_CORA.data)\n",
        "print(f'\\nGNN test accuracy on CORA: {acc*100:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmRisO1_tSZk",
        "outputId": "099ba49a-fbb0-4583-b345-fddaa11ad2ff"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "GNN test accuracy on CORA: 75.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **GNN on the Facebook Page-Page Dataset**"
      ],
      "metadata": {
        "id": "jBE50DGAtjsV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "adjacency=adjacency_fbpp\n",
        "gnn_fb = VanillaGNN(dataset_FBPP.num_features, 16, dataset_FBPP.num_classes)"
      ],
      "metadata": {
        "id": "ozRr_ldFtScO"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gnn_fb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eONrOmRotSex",
        "outputId": "855c4f2c-1b94-4e7a-ab07-9b89e70d6aa4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VanillaGNN(\n",
              "  (gnn1): VanillaGNNLayer(\n",
              "    (linear): Linear(in_features=128, out_features=16, bias=False)\n",
              "  )\n",
              "  (gnn2): VanillaGNNLayer(\n",
              "    (linear): Linear(in_features=16, out_features=4, bias=False)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gnn_fb.fit(dataset_FBPP.data, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MP_H6O3Lttk0",
        "outputId": "df16bd14-b2ce-45e9-f2b7-973c203d1fc2"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch   0 | Train Loss: 176.683 | Train Acc: 28.31% | Val Loss: 173.10 | Val Acc: 28.41%\n",
            "Epoch  20 | Train Loss: 6.675 | Train Acc: 79.69% | Val Loss: 4.49 | Val Acc: 80.19%\n",
            "Epoch  40 | Train Loss: 2.284 | Train Acc: 82.15% | Val Loss: 1.60 | Val Acc: 83.64%\n",
            "Epoch  60 | Train Loss: 1.233 | Train Acc: 83.91% | Val Loss: 1.06 | Val Acc: 84.34%\n",
            "Epoch  80 | Train Loss: 0.812 | Train Acc: 84.94% | Val Loss: 0.78 | Val Acc: 84.79%\n",
            "Epoch 100 | Train Loss: 1.321 | Train Acc: 84.33% | Val Loss: 0.96 | Val Acc: 84.39%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = gnn_fb.test(dataset_FBPP.data)\n",
        "print(f'\\nGNN test accuracy for Facebook Page Page Dataset: {acc*100:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVpJo_Nbttoa",
        "outputId": "ce475d79-20a7-4015-df37-c81dac9dbb1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "GNN test accuracy: 82.67%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Let's move to the Next Part of this tutorial and consider other graphs**"
      ],
      "metadata": {
        "id": "Jv0gZHwEvG0F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **CiteSeer and Pubmed dataset**"
      ],
      "metadata": {
        "id": "v_Nkqob7znOO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CiteSeer Dataset:** This is a citation network graph where nodes represent academic papers and edges represent citations between these papers. Each node is described by a text feature vector extracted from the content of the paper, and nodes are classified into one of several academic topics. The dataset is typically used to evaluate performance of graph-based machine learning algorithms. There are 6 classes, each representing a different topic or field within academic papers.\n",
        "\n",
        "**PubMed Dataset:** Similar to the CiteSeer, PubMed is also a citation graph but specifically comprises scientific papers related to diabetes from the PubMed database. Each node in the graph represents a paper and comes with a feature vector derived from a TF-IDF weighted word vector from the abstract of the paper. Nodes are labeled with one of three classes, indicating the type of diabetes they concern. This dataset is also widely used in graph neural network research. There are 3 classes, which categorize the papers based on different types of diabetes research topics."
      ],
      "metadata": {
        "id": "Bu-RmicN1F9w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Graph             | Nodes    | Edges |Classes |\n",
        "| :---------------: | :------: | :---: | :---:  |\n",
        "| Zachary Karate Club|   34   | 78  |    2  (not predefined) |\n",
        "| Cora          |   2,708   | 5,429 |       7 |\n",
        "| FB Page-Page    |  22,470   | 171,002 |     4   |\n",
        "| -   |  -   | - |     -   |\n",
        "| CiteSeer   |  3,327   | 4,732 |     6   |\n",
        "| Pubmed    |  19,717   | 44,338 |     3   |"
      ],
      "metadata": {
        "id": "ygarLc1z1yh1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_CSeer = Planetoid(root=\".\", name=\"CiteSeer\")"
      ],
      "metadata": {
        "id": "FUsNqCFNttqz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84fcd603-425d-4c5f-a8ef-fdaaeebe81e9"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.x\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.tx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.allx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.y\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.ty\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.ally\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.graph\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.test.index\n",
            "Processing...\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_graph_CSeer = dataset_CSeer[0] # get the citeseer graph"
      ],
      "metadata": {
        "id": "dRcGahpQttsU"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print information about the dataset\n",
        "print(f'Dataset: {dataset_CSeer}')\n",
        "print('---------------')\n",
        "print(f'Number of graphs: {len(dataset_CSeer)}')\n",
        "print(f'Number of nodes: {data_graph_CSeer.x.shape[0]}')\n",
        "print(f'Number of features: {dataset_CSeer.num_features}')\n",
        "print(f'Number of classes: {dataset_CSeer.num_classes}')\n",
        "\n",
        "# Print information about the graph\n",
        "print(f'\\nGraph:')\n",
        "print('------')\n",
        "print(f'Edges are directed: {data_graph_CSeer.is_directed()}')\n",
        "print(f'Graph has isolated nodes: {data_graph_CSeer.has_isolated_nodes()}')\n",
        "print(f'Graph has loops: {data_graph_CSeer.has_self_loops()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPMDiDf31a3p",
        "outputId": "4a95ae5e-414d-49fa-850b-c30ddf0a39b0"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset: CiteSeer()\n",
            "---------------\n",
            "Number of graphs: 1\n",
            "Number of nodes: 3327\n",
            "Number of features: 3703\n",
            "Number of classes: 6\n",
            "\n",
            "Graph:\n",
            "------\n",
            "Edges are directed: False\n",
            "Graph has isolated nodes: True\n",
            "Graph has loops: False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_PM = Planetoid(root=\".\", name=\"PubMed\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMytPdkm1a6h",
        "outputId": "69880cc3-12d3-4661-cdc9-d5020509e445"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.pubmed.x\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.pubmed.tx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.pubmed.allx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.pubmed.y\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.pubmed.ty\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.pubmed.ally\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.pubmed.graph\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.pubmed.test.index\n",
            "Processing...\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_graph_PM = dataset_PM[0] # the Pubmed graph"
      ],
      "metadata": {
        "id": "wsBeCXF51a9Q"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print information about the dataset\n",
        "print(f'Dataset: {dataset_PM}')\n",
        "print('---------------')\n",
        "print(f'Number of graphs: {len(dataset_PM)}')\n",
        "print(f'Number of nodes: {data_graph_PM.x.shape[0]}')\n",
        "print(f'Number of features: {dataset_PM.num_features}')\n",
        "print(f'Number of classes: {dataset_PM.num_classes}')\n",
        "\n",
        "# Print information about the graph\n",
        "print(f'\\nGraph:')\n",
        "print('------')\n",
        "print(f'Edges are directed: {data_graph_PM.is_directed()}')\n",
        "print(f'Graph has isolated nodes: {data_graph_PM.has_isolated_nodes()}')\n",
        "print(f'Graph has loops: {data_graph_PM.has_self_loops()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APPh7kaZ3OTO",
        "outputId": "5d54c5bf-fd05-4715-8bba-4b0a2497a38d"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset: PubMed()\n",
            "---------------\n",
            "Number of graphs: 1\n",
            "Number of nodes: 19717\n",
            "Number of features: 500\n",
            "Number of classes: 3\n",
            "\n",
            "Graph:\n",
            "------\n",
            "Edges are directed: False\n",
            "Graph has isolated nodes: False\n",
            "Graph has loops: False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **MLP on CiteSeer**"
      ],
      "metadata": {
        "id": "aEkr1mVp1qo6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_x = pd.DataFrame(data_graph_CSeer.x.numpy())\n",
        "\n",
        "df_x['label'] = pd.DataFrame(data_graph_CSeer.y)"
      ],
      "metadata": {
        "id": "BsKPMvsN1bCp"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "F9h4GCYI1bIK",
        "outputId": "0ad676e5-2de8-45fd-f848-9007f17996b5"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        0    1    2    3    4    5    6    7    8    9  ...  3694  3695  3696  \\\n",
              "0     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
              "1     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
              "2     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
              "3     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
              "4     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
              "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   \n",
              "3322  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
              "3323  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
              "3324  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
              "3325  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
              "3326  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
              "\n",
              "      3697  3698  3699  3700  3701  3702  label  \n",
              "0      0.0   0.0   0.0   0.0   0.0   0.0      3  \n",
              "1      0.0   0.0   0.0   0.0   0.0   0.0      1  \n",
              "2      0.0   0.0   0.0   0.0   0.0   0.0      5  \n",
              "3      0.0   0.0   0.0   0.0   0.0   0.0      5  \n",
              "4      0.0   0.0   0.0   0.0   0.0   0.0      3  \n",
              "...    ...   ...   ...   ...   ...   ...    ...  \n",
              "3322   0.0   0.0   0.0   0.0   0.0   0.0      3  \n",
              "3323   0.0   0.0   0.0   0.0   0.0   0.0      3  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_x"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlp = MLP(dataset_CSeer.num_features, 16, dataset_CSeer.num_classes)"
      ],
      "metadata": {
        "id": "OSPbS6xZ1bKy"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlp.fit(dataset_CSeer.data, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJZCknNo1bOB",
        "outputId": "122f54be-4aeb-4e53-f8ba-3a8ebe67cfd5"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch_geometric/data/in_memory_dataset.py:300: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch   0 | Train Loss: 1.802 | Train Acc: 16.67% | Val Loss: 1.82 | Val Acc: 18.80%\n",
            "Epoch  20 | Train Loss: 0.008 | Train Acc: 100.00% | Val Loss: 1.43 | Val Acc: 47.80%\n",
            "Epoch  40 | Train Loss: 0.001 | Train Acc: 100.00% | Val Loss: 1.48 | Val Acc: 46.20%\n",
            "Epoch  60 | Train Loss: 0.003 | Train Acc: 100.00% | Val Loss: 1.34 | Val Acc: 48.20%\n",
            "Epoch  80 | Train Loss: 0.005 | Train Acc: 100.00% | Val Loss: 1.29 | Val Acc: 50.80%\n",
            "Epoch 100 | Train Loss: 0.006 | Train Acc: 100.00% | Val Loss: 1.28 | Val Acc: 52.60%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = mlp.test(dataset_CSeer.data)"
      ],
      "metadata": {
        "id": "SWnTy6Sy1bQp"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\nMLP on Citeseer - test accuracy: {acc*100:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CryFrmDM1bTZ",
        "outputId": "1cea1971-4953-4682-e0c5-5fa6089295f0"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "MLP on Citeseer - test accuracy: 52.70%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **MLP on PubMed graph**"
      ],
      "metadata": {
        "id": "zriGG3iN37Qf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mlp_pm = MLP(dataset_PM.num_features, 16, dataset_PM.num_classes)"
      ],
      "metadata": {
        "id": "YHHVuEFk1bWU"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlp_pm.fit(dataset_PM.data, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Y2treGW4uVs",
        "outputId": "2ae84175-9dd6-4188-ac5e-47c486c19816"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch   0 | Train Loss: 1.113 | Train Acc: 33.33% | Val Loss: 1.16 | Val Acc: 19.60%\n",
            "Epoch  20 | Train Loss: 0.603 | Train Acc: 100.00% | Val Loss: 0.93 | Val Acc: 65.20%\n",
            "Epoch  40 | Train Loss: 0.113 | Train Acc: 100.00% | Val Loss: 0.75 | Val Acc: 69.00%\n",
            "Epoch  60 | Train Loss: 0.050 | Train Acc: 100.00% | Val Loss: 0.71 | Val Acc: 71.00%\n",
            "Epoch  80 | Train Loss: 0.045 | Train Acc: 100.00% | Val Loss: 0.70 | Val Acc: 71.00%\n",
            "Epoch 100 | Train Loss: 0.038 | Train Acc: 100.00% | Val Loss: 0.69 | Val Acc: 70.80%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = mlp_pm.test(dataset_PM.data)\n",
        "print(f'\\nMLP on PubMed test accuracy: {acc*100:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0JHUVdz4xjs",
        "outputId": "ea493419-9255-487b-d2e8-23394d837a80"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "MLP on PubMed test accuracy: 71.90%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Vanilla GNN on Citeseer and Pubmed**"
      ],
      "metadata": {
        "id": "UkMZOZzN42LN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "adjacency_cseer = to_dense_adj(dataset_CSeer.data.edge_index)[0]\n",
        "adjacency_cseer += torch.eye(len(adjacency_cseer))\n",
        "adjacency_cseer\n",
        "\n",
        "\n",
        "\n",
        "adjacency_pm = to_dense_adj(dataset_PM.data.edge_index)[0]\n",
        "adjacency_pm += torch.eye(len(adjacency_pm))\n",
        "adjacency_pm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7erhMfsN4xmu",
        "outputId": "4af39688-1fff-4310-ecb1-411cc56c9c19"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1., 0., 0.,  ..., 0., 0., 0.],\n",
              "        [0., 1., 0.,  ..., 0., 0., 0.],\n",
              "        [0., 0., 1.,  ..., 0., 0., 0.],\n",
              "        ...,\n",
              "        [0., 0., 0.,  ..., 1., 0., 0.],\n",
              "        [0., 0., 0.,  ..., 0., 1., 0.],\n",
              "        [0., 0., 0.,  ..., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adjacency_cseer.shape, adjacency_pm.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3WHMLGlU4xpf",
        "outputId": "52e50043-5bfb-4d8e-c9db-e0da97b5af3b"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([3327, 3327]), torch.Size([19717, 19717]))"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adjacency = adjacency_cseer\n",
        "gnn = VanillaGNN(dataset_CSeer.num_features, 16, dataset_CSeer.num_classes)\n",
        "print(gnn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I76M8YDS4xsH",
        "outputId": "080b4509-73cc-4fc3-8f64-fcb33647f775"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VanillaGNN(\n",
            "  (gnn1): VanillaGNNLayer(\n",
            "    (linear): Linear(in_features=3703, out_features=16, bias=False)\n",
            "  )\n",
            "  (gnn2): VanillaGNNLayer(\n",
            "    (linear): Linear(in_features=16, out_features=6, bias=False)\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gnn.fit(dataset_CSeer.data, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEZwzXtw4xuv",
        "outputId": "ea1593bb-9605-4175-fbab-81cafd02fd7a"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch   0 | Train Loss: 1.855 | Train Acc: 17.50% | Val Loss: 1.87 | Val Acc: 16.20%\n",
            "Epoch  20 | Train Loss: 0.001 | Train Acc: 100.00% | Val Loss: 3.25 | Val Acc: 62.20%\n",
            "Epoch  40 | Train Loss: 0.000 | Train Acc: 100.00% | Val Loss: 3.57 | Val Acc: 65.00%\n",
            "Epoch  60 | Train Loss: 0.000 | Train Acc: 100.00% | Val Loss: 3.40 | Val Acc: 65.80%\n",
            "Epoch  80 | Train Loss: 0.000 | Train Acc: 100.00% | Val Loss: 3.17 | Val Acc: 65.80%\n",
            "Epoch 100 | Train Loss: 0.000 | Train Acc: 100.00% | Val Loss: 2.97 | Val Acc: 66.80%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test\n",
        "acc = gnn.test(dataset_CSeer.data)\n",
        "print(f'\\nGNN on CiteSeer test accuracy: {acc*100:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXfM2OEl4xxd",
        "outputId": "c663e941-f515-48f8-d677-446cb1d091a5"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "GNN on CiteSeer test accuracy: 65.30%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adjacency=adjacency_pm\n",
        "gnn_pm = VanillaGNN(dataset_PM.num_features, 16, dataset_PM.num_classes)"
      ],
      "metadata": {
        "id": "5Q4lxcNr4xz-"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gnn_pm.fit(dataset_PM.data, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QFWRaUtz4x32",
        "outputId": "e82a37f1-0327-4b51-a4a5-4f01ed13da08"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch   0 | Train Loss: 1.123 | Train Acc: 23.33% | Val Loss: 1.14 | Val Acc: 24.40%\n",
            "Epoch  20 | Train Loss: 0.064 | Train Acc: 100.00% | Val Loss: 0.84 | Val Acc: 78.00%\n",
            "Epoch  40 | Train Loss: 0.004 | Train Acc: 100.00% | Val Loss: 1.25 | Val Acc: 79.00%\n",
            "Epoch  60 | Train Loss: 0.002 | Train Acc: 100.00% | Val Loss: 1.29 | Val Acc: 79.20%\n",
            "Epoch  80 | Train Loss: 0.002 | Train Acc: 100.00% | Val Loss: 1.27 | Val Acc: 79.40%\n",
            "Epoch 100 | Train Loss: 0.002 | Train Acc: 100.00% | Val Loss: 1.23 | Val Acc: 79.40%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = gnn_pm.test(dataset_PM.data)\n",
        "print(f'\\nGNN on PubMed test accuracy: {acc*100:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UN1_0LAX5Z0q",
        "outputId": "f0af834c-1636-449a-82a3-bc70871e525a"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "GNN on PubMed test accuracy: 76.50%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **In Summary**"
      ],
      "metadata": {
        "id": "b9jsVbMI7fpF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Dataset  | MLP             | GNN              | Outperformance ⬆|\n",
        "|:----------:|:-----------------:|:------------------:| :-------------: |\n",
        "| Cora     | 53.40% | 75.00%  |21.6 % |\n",
        "| Facebook Page-Page | 75.13% | 82.67%  | 7.54%|\n",
        "| CiteSeer | 52.70% | 65.50%  | 12.8% |\n",
        "| PubMed   | 71.90% | 76.50%  | 4.6%|"
      ],
      "metadata": {
        "id": "HsJEwwYk7muN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Thank You! ✈**"
      ],
      "metadata": {
        "id": "IvhBGexA-qEC"
      }
    }
  ]
}