{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab PyG\n",
    "\n",
    "### Antonio Longa \n",
    "### antonio.longa@unitn.it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pytorch Geometric (PyG) \n",
    "\n",
    "Pytorch Geometric (PyG) is a geometric deep learning extension library for PyTorch. It consists of various methods for deep learning on graphs and other irregular structures. It implements plenty of graph neural networks from the literature and allows to easily prototype new ones.\n",
    "\n",
    "Adapted from tutorials and notebooks from https://github.com/rusty1s/pytorch_geometric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Message Passing Networks\n",
    "\n",
    "\n",
    "![title](img/img1.png)\n",
    "\n",
    "\n",
    "Graph neural networks can be defined in terms of a *neighborhood aggregation* or *message passing* scheme.\n",
    "With $\\mathbf{x}^{(k-1)}_i \\in \\mathbb{R}^F$ denoting node features of node $i$ in layer $(k-1)$ and $\\mathbf{e}_{j,i} \\in \\mathbb{R}^D$ denoting (optional) edge features from node $j$ to node $i$, message passing graph neural networks can be described as\n",
    "\n",
    "\n",
    "\n",
    "$$\n",
    "  \\mathbf{x}_i^{(k)} = \\gamma^{(k)} \\left( \\mathbf{x}_i^{(k-1)}, \\square_{j \\in \\mathcal{N}(i)} \\, \\phi^{(k)}\\left(\\mathbf{x}_i^{(k-1)}, \\mathbf{x}_j^{(k-1)},\\mathbf{e}_{j,i}\\right) \\right),\n",
    "$$\n",
    "\n",
    "where $\\square$ denotes a differentiable, permutation invariant function, *e.g.*, sum, mean or max, and $\\gamma$ and $\\phi$ denote differentiable functions such as MLPs (Multi Layer Perceptrons)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The \"MessagePassing\" Base Class\n",
    "\n",
    "PyTorch Geometric provides the `MessagePassing` base class, which helps in creating such kinds of message passing graph neural networks by automatically taking care of message propagation.\n",
    "The user only has to define the functions $\\phi$ , *i.e.* `message`, and $\\gamma$ , *i.e.* `update`, as well as the aggregation scheme to use, *i.e.* `aggr=\"add\"`, `aggr=\"mean\"` or `aggr=\"max\"`.\n",
    "\n",
    "<div>\n",
    "<img src=\"img/img2.png\" width=\"500\"/>\n",
    "</div>\n",
    "\n",
    "\n",
    "This is done with the help of the following methods:\n",
    "\n",
    "\n",
    "* `MessagePassing(aggr=\"add\", flow=\"source_to_target\", node_dim=-2)`: Defines the aggregation scheme to use (`\"add\"`, `\"mean\"` or `\"max\"`) and the flow direction of message passing (either `\"source_to_target\"` or `\"target_to_source\"`).\n",
    "  Furthermore, the `node_dim` attribute indicates along which axis to propagate.\n",
    "* `MessagePassing.propagate(edge_index, ...)`:\n",
    "  The initial call to start propagating messages.\n",
    "  Takes in the edge indices and all additional data which is needed to construct messages and to update node embeddings. \n",
    "* `MessagePassing.message(...)`: Constructs messages to node $i$ in analogy to $\\phi$ for each edge in $(j,i) \\in \\mathcal{E}$ if `flow=\"source_to_target\"` and $(i,j) \\in \\mathcal{E}$ if `flow=\"target_to_source\"`.\n",
    "* `MessagePassing.update(aggr_out, ...)`: Updates node embeddings in analogy to $\\gamma$ for each node $i \\in \\mathcal{V}$.\n",
    "  Takes in the output of aggregation as first argument and any argument which was initially passed to `MessagePassing.propagate`.\n",
    "\n",
    "Let us verify this by re-implementing two popular GNN variants, the `GCN layer from Kipf and Welling <https://arxiv.org/abs/1609.02907>`_ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### edge index?\n",
    "\n",
    "\\begin{equation}\n",
    "Adj = \n",
    "\\begin{pmatrix}\n",
    "1 & 1 & 0\\\\\n",
    "1 & 1 & 0\\\\\n",
    "0 & 0 & 1\n",
    "\\end{pmatrix}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "edgeIndex = \n",
    "\\begin{pmatrix}\n",
    "0 & 0 & 1 & 1 & 2\\\\\n",
    "0 & 1 & 0 & 1 & 2\n",
    "\\end{pmatrix}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing the GCN Layer\n",
    "\n",
    "The GCN layer from Kipf and Welling is a popular GNN that was the first \n",
    "to bring the idea of layerwise convolutions to graphs processing. The layer\n",
    "is mathematically defined as\n",
    "\n",
    "$$\n",
    "    \\mathbf{x}_i^{(k)} = \\sum_{j \\in \\mathcal{N}(i) \\cup \\{ i \\}} \\frac{1}{\\sqrt{\\deg(i)} \\cdot \\sqrt{\\deg(j)}} \\cdot \\left( \\mathbf{\\Theta} \\cdot \\mathbf{x}_j^{(k-1)} \\right),\n",
    "$$\n",
    "\n",
    "where neighboring node features are first transformed by a weight matrix $\\mathbf{\\Theta}$, normalized by their degree, and finally summed up.\n",
    "This formula can be divided into the following steps:\n",
    "\n",
    "1. Add self-loops to the adjacency matrix.\n",
    "2. Linearly transform node feature matrix.\n",
    "3. Compute normalization coefficients.\n",
    "4. Normalize node features in $\\phi$.\n",
    "5. Sum up neighboring node features (`\"add\"` aggregation).\n",
    "\n",
    "Steps 1-3 are typically computed before message passing takes place.\n",
    "Steps 4-5 can be easily processed using the `MessagePassing` base class.\n",
    "The full layer implementation is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install required packages.\n",
    "#!pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html\n",
    "#!pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html\n",
    "#!pip install -q torch-geometric\n",
    "\n",
    "import torch\n",
    "from torch_geometric.nn import MessagePassing\n",
    "from torch_geometric.utils import add_self_loops, degree\n",
    "\n",
    "class myGCNConv(MessagePassing):\n",
    "    def __init__(self, in_channels, out_channels):\n",
    "        super(myGCNConv, self).__init__(aggr='add')  # \"Add\" aggregation (Step 5).\n",
    "        self.lin = torch.nn.Linear(in_channels, out_channels)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        # x has shape [N, in_channels]\n",
    "        # edge_index has shape [2, E] (sparse adjacency matrix as a list of edges)\n",
    "\n",
    "        # Step 1: Add self-loops to the adjacency matrix [edge list].\n",
    "        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))\n",
    "\n",
    "        # Step 2: Linearly transform node feature matrix.\n",
    "        x = self.lin(x)\n",
    "\n",
    "        # Step 3: Compute normalization.\n",
    "        row, col = edge_index\n",
    "        deg = degree(col, x.size(0), dtype=x.dtype)\n",
    "        deg_inv_sqrt = deg.pow(-0.5)\n",
    "        deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0\n",
    "        norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]\n",
    "\n",
    "        # Step 4-5: Start propagating messages.\n",
    "        return self.propagate(edge_index, x=x, norm=norm)\n",
    "\n",
    "    def message(self, x_j, norm):\n",
    "        # x_j has shape [E, out_channels]\n",
    "\n",
    "        # Step 4: Normalize node features.\n",
    "        return norm.view(-1, 1) * x_j\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "zF5bw3m9UrMy"
   },
   "outputs": [],
   "source": [
    "# Helper function for visualization.\n",
    "%matplotlib inline\n",
    "import torch\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def visualize(h, color, epoch=None, loss=None):\n",
    "    plt.figure(figsize=(7,7))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    if torch.is_tensor(h):\n",
    "        h = h.detach().cpu().numpy()\n",
    "        plt.scatter(h[:, 0], h[:, 1], s=140, c=color, cmap=\"Set2\")\n",
    "        if epoch is not None and loss is not None:\n",
    "            plt.xlabel(f'Epoch: {epoch}, Loss: {loss.item():.4f}', fontsize=16)\n",
    "    else:\n",
    "        nx.draw_networkx(G, pos=nx.spring_layout(G, seed=42), with_labels=False,\n",
    "                         node_color=color, cmap=\"Set2\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qoW2Z7P70LNQ"
   },
   "source": [
    "# Example: node classification\n",
    "\n",
    "Following [Kipf et al. (2017)](https://arxiv.org/abs/1609.02907), let's dive into the world of GNNs by looking at a simple graph-structured example, the well-known [**Zachary's karate club network**](https://en.wikipedia.org/wiki/Zachary%27s_karate_club). This graph describes a social network of 34 members of a karate club and documents links between members who interacted outside the club. Here, we are interested in detecting communities that arise from the member's interaction.\n",
    "\n",
    "PyTorch Geometric provides an easy access to this dataset via the [`torch_geometric.datasets`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets) subpackage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H_VTFHd0uFz6",
    "outputId": "e9472a23-e3c8-4ef2-86c1-64282d80de5d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: KarateClub():\n",
      "======================\n",
      "Number of graphs: 1\n",
      "Number of features: 34\n",
      "Number of classes: 4\n"
     ]
    }
   ],
   "source": [
    "from torch_geometric.datasets import KarateClub\n",
    "\n",
    "dataset = KarateClub()\n",
    "print(f'Dataset: {dataset}:')\n",
    "print('======================')\n",
    "print(f'Number of graphs: {len(dataset)}')\n",
    "print(f'Number of features: {dataset.num_features}')\n",
    "print(f'Number of classes: {dataset.num_classes}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(x=[34, 34], edge_index=[2, 156], y=[34], train_mask=[34])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7cjjyFVnpKB0"
   },
   "source": [
    "After initializing the [`KarateClub`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.KarateClub) dataset, we first can inspect some of its properties.\n",
    "For example, we can see that this dataset holds exactly **one graph**, and that each node in this dataset is assigned a **34-dimensional feature vector** (which uniquely describes the members of the karate club).\n",
    "Furthermore, the graph holds exactly **4 classes**, which represent the community each node belongs to.\n",
    "\n",
    "Let's now look at the underlying graph in more detail:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gUFSrDPxuQ23",
    "outputId": "98c222c0-d955-4607-8b0e-cb98c012cb79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data(x=[34, 34], edge_index=[2, 156], y=[34], train_mask=[34])\n",
      "==============================================================\n",
      "Number of nodes: 34\n",
      "Number of edges: 156\n",
      "Average node degree: 4.59\n",
      "Number of training nodes: 4\n",
      "Training node label rate: 0.12\n",
      "Contains isolated nodes: False\n",
      "Contains self-loops: False\n",
      "Is undirected: True\n"
     ]
    }
   ],
   "source": [
    "data = dataset[0]  # Get the first graph object.\n",
    "\n",
    "print(data)\n",
    "print('==============================================================')\n",
    "\n",
    "# Gather some statistics about the graph.\n",
    "print(f'Number of nodes: {data.num_nodes}')\n",
    "print(f'Number of edges: {data.num_edges}')\n",
    "print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')\n",
    "print(f'Number of training nodes: {data.train_mask.sum()}')\n",
    "print(f'Training node label rate: {int(data.train_mask.sum()) / data.num_nodes:.2f}')\n",
    "print(f'Contains isolated nodes: {data.has_isolated_nodes()}')\n",
    "print(f'Contains self-loops: {data.has_self_loops()}')\n",
    "print(f'Is undirected: {data.is_undirected()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MY4pZma9p3Ax"
   },
   "source": [
    "Each graph in PyTorch Geometric is represented by a single [`Data`](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.Data) object, which holds all the information to describe its graph representation.\n",
    "We can print the data object anytime via `print(data)` to receive a short summary about its attributes and their shapes:\n",
    "```\n",
    "Data(edge_index=[2, 156], x=[34, 34], y=[34], train_mask=[34])\n",
    "```\n",
    "We can see that this `data` object holds 4 attributes:\n",
    "(1) The `edge_index` property holds the information about the **graph connectivity**, *i.e.*, a tuple of source and destination node indices for each edge.\n",
    "PyG further refers to (2) **node features** as `x` (each of the 34 nodes is assigned a 34-dim feature vector), and to (3) **node labels** as `y` (each node is assigned to exactly one class).\n",
    "(4) There also exists an additional attribute called `train_mask`, which describes for which nodes we already know their community assigments.\n",
    "In total, we are only aware of the ground-truth labels of 4 nodes (one for each community), and the task is to infer the community assignment for the remaining nodes.\n",
    "\n",
    "\n",
    "We can further visualize the graph by converting it to the `networkx` library format, which implements, in addition to graph manipulation functionalities, powerful tools for visualization:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**to networkx**\n",
    "<div>\n",
    "<img src=\"img/img3.png\" width=\"500\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "Y9MOs8iSwKFD",
    "outputId": "7bd4a2ef-08a4-458c-dfb5-09ea82fed059"
   },
   "outputs": [],
   "source": [
    "from torch_geometric.utils import to_networkx\n",
    "\n",
    "G = to_networkx(data,node_attrs=[\"x\"], to_undirected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x': [1.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0]}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "\n",
    "G.nodes(data=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = to_networkx(data, to_undirected=True)\n",
    "\n",
    "visualize(G, color=data.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What we did so far...**\n",
    "\n",
    "1) define our conv: myGCNConv  \n",
    "2) download karate dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kPbYXBn1yYIJ"
   },
   "source": [
    "## Implementing a GCN \n",
    "\n",
    "We implement a GCN as a `torch.nn.Module` class that contains a sequence of GCNConv layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AkQAVluLuxT_",
    "outputId": "b049292d-b2da-4926-f974-776012a3ec60"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): myGCNConv()\n",
      "  (conv2): myGCNConv()\n",
      "  (conv3): myGCNConv()\n",
      "  (classifier): Linear(in_features=2, out_features=4, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch.nn import Linear\n",
    "#from torch_geometric.nn import GCNConv\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(112233)\n",
    "        self.conv1 = myGCNConv(dataset.num_features, 4)\n",
    "        self.conv2 = myGCNConv(4, 4)\n",
    "        self.conv3 = myGCNConv(4, 2)\n",
    "        self.classifier = Linear(2, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        h = self.conv1(x, edge_index)\n",
    "        h = h.tanh()\n",
    "        h = self.conv2(h, edge_index)\n",
    "        h = h.tanh()\n",
    "        h = self.conv3(h, edge_index)\n",
    "        h = h.tanh()  # Final GNN embedding space.\n",
    "        \n",
    "        # Apply a final (linear) classifier.\n",
    "        out = self.classifier(h)\n",
    "\n",
    "        return out, h\n",
    "\n",
    "model = GCN()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hjsb3Fst2P8k"
   },
   "source": [
    "Here, we first initialize all of our building blocks in `__init__` and define the computation flow of our network in `forward`.\n",
    "We first define and stack **three graph convolution layers**, which corresponds to aggregating 3-hop neighborhood information around each node (all nodes up to 3 \"hops\" away).\n",
    "In addition, the `myGCNConv` layers reduce the node feature dimensionality to $2$, *i.e.*, $34 \\rightarrow 4 \\rightarrow 4 \\rightarrow 2$. Each `myGCNConv` layer is enhanced by a [tanh](https://pytorch.org/docs/stable/generated/torch.nn.Tanh.html?highlight=tanh#torch.nn.Tanh) non-linearity.\n",
    "\n",
    "After that, we apply a single linear transformation ([`torch.nn.Linear`](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html?highlight=linear#torch.nn.Linear)) that acts as a classifier to map our nodes to 1 out of the 4 classes/communities.\n",
    "\n",
    "We return both the output of the final classifier as well as the final node embeddings produced by our GNN.\n",
    "We proceed to initialize our final model via `GCN()`, and printing our model produces a summary of all its used sub-modules.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dt8fPEmc4m5l"
   },
   "source": [
    "### Embedding the Karate Club Network\n",
    "\n",
    "Let's take a look at the node embeddings produced by our GNN.\n",
    "Here, we pass in the initial node features `x` and the graph connectivity information `edge_index` to the model, and visualize its 2-dimensional embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "id": "nwHtX5siwe2v",
    "outputId": "080bbcb1-8278-4dc5-dd4c-f163e9bb8372"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding shape: [34, 2]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = GCN()\n",
    "\n",
    "out, h = model(data.x, data.edge_index)\n",
    "print(f'Embedding shape: {list(h.shape)}')\n",
    "\n",
    "visualize(h, color=data.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([34, 4])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out #.argmax(-1)\n",
    "\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1-W5Kfhu5I-P"
   },
   "source": [
    "Remarkably, even before training the weights of our model, the model produces an embedding of nodes that closely resembles the community-structure of the graph.\n",
    "Nodes of the same color (community) are already closely clustered together in the embedding space, although the weights of our model are initialized **completely at random** and we have not yet performed any training so far!\n",
    "This leads to the conclusion that GNNs introduce a strong inductive bias, leading to similar embeddings for nodes that are close to each other in the input graph.\n",
    "\n",
    "### Training on the Karate Club Network\n",
    "\n",
    "But can we do better? Let's look at an example on how to train our network parameters based on the knowledge of the community assignments of 4 nodes in the graph (one for each community):\n",
    "\n",
    "Since everything in our model is differentiable and parameterized, we can add some labels, train the model and observse how the embeddings react.\n",
    "Here, we make use of a semi-supervised or transductive learning procedure: We simply train against one node per class, but are allowed to make use of the complete input graph data.\n",
    "\n",
    "\n",
    "\n",
    "Training our model is very similar to any other PyTorch model.\n",
    "In addition to defining our network architecture, we define a loss critertion (here, [`CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html)) and initialize a stochastic gradient optimizer (here, [`Adam`](https://pytorch.org/docs/stable/optim.html?highlight=adam#torch.optim.Adam)).\n",
    "\n",
    "Note that our semi-supervised learning scenario is achieved by the following line:\n",
    "```\n",
    "loss = criterion(out[data.train_mask], data.y[data.train_mask])\n",
    "```\n",
    "While we compute node embeddings for all of our nodes, we **only make use of the training nodes for computing the loss**.\n",
    "Here, this is implemented by filtering the output of the classifier `out` and ground-truth labels `data.y` to only contain the nodes in the `train_mask`.\n",
    "\n",
    "Let us now start training and see how our node embeddings evolve over time (best experienced by explicitely running the code):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "id": "etxOsz8QIbMO",
    "outputId": "a961884c-7520-460a-d591-94834563c4a2",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAGdCAYAAAAv9mXmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAhY0lEQVR4nO3deXAc533m8efXM4P7IAASJHhTvA/LOkhJa+v0IVmHb9mxbGWtZB072VRtXLupTbKbje0c3qpoKymnYseu2LEsK77WsWOtLNuyLFGyrIuHTFKkIIqkKJ4AQQAECYAAZqbf/aMH9HAIEANyXgxn8P1UTQEz/U73Dw2gn+737e4x55wAACi0oNgFAADKEwEDAPCCgAEAeEHAAAC8IGAAAF7EJ9N45syZbvHixZ5KAQCUmi1bthx3zs0aa9qkAmbx4sXavHlzYaoCAJQ8M3tjvGl0kQEAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvJjUlfwAgNLVPdSv7+zdol29RxU6p8BMyxpm6beWXq25tTMKvjwCBgCmgX9pf1YvdO0/67XQObX3depzWx/VqsbZ+qN1tygICtexRRcZAJS5r7X/6pxwydXe16m/2/GLgi6XgAGAMtYxeFIvdo17P8qzvHayS+29HQVbNgEDAGXswd3PS3J5t//evi0FWzYBAwBlbO+p45Is7/aHB/sKtmwCBgDKWv5HLxfWfnwEDACUqeGRYRUyMCaLgAGAMrW9a7vmxbqUf8g4VWuoYMsnYACgTL189Gl9uP6JSb3n5pqtBVs+AQMAZapWB7S04ogWxDo18VGMU1NwUldX7S7Y8gkYAChDzx3dqxNhvZykP21+UHPPdJXlBk30WlNwSp9p/hel3MyC1UDAAEAZ+vmhV5V0CW06vUayQJ+Z+YA+0fD/NDM4od8EjVNj0K+P1f9Mn2/5sgJzUvPvFKwG7kUGAGUoFYaKKa0nBq/UhupXJIXaUN2uDdXtkiTnJMu6PCbtTD3pBs1ofmvBauAIBgDK0MLqZh1Pz1BHeqa+f+pmDbuzjydyw+W0q9SDfe9SU1VtwWogYACgDN235lodTM3WvPgx/fL0m/Vg3+06ma7R6TBxpk3KBRpxcb2enKv7u+/R8eFZBa2BLjIAKEPxeExBGOhEql41Nqytwyu0ZXiV1lbs0+qK/aq0EfWGDXpxaI360rWqCYakqsbC1lDQuQEALhl3zF2jRzp2qk4DmhU7oZ50g3aNLNbLI0slSRUaUahAdTaowcFmvX350oIuny4yAChTb1uyWrHQ1B/WqjPVooSSaot1qzXWrdagRzU2pJSLa2CgRa7G6aa25QVdPgEDAGWqNlGh+9Zcp0QspsqBSg0l63Q41apjqRZ1JVs0fLJVQRjI6kLdveRKNVXWFHT5dJEBQBm7pnWJAgv04GsvKOECDYehJMnFnNKNKcVl+q2lV+v6OcsKvmwCBgDK3PpZi/Tmlvna0nVAz3Xu00BqRDXxCl3TulgbZi1SZcxPFBAwADANJIKYrpu9RNfNXjJly2QMBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAX8WIXAAAovFQ61Gtv9GrLzg6dODUik9TUWKn1a+do6cIZigX+jy8IGAAoM8d7T+v/PvaqUqlQyVR45vWO44P62a/2q3JTTB+6baWaGqq81kEXGQCUkROnhvWdn7Tr9FDqrHAZlUyFGhhM6tuPtqt/cMRrLQQMAJSRJ188oGQyfd42TtLwSErPbD3stRYCBgDKxMDppA4cOSmXR1vnpFf392h4JOWtHgIGAMrE/sN9CgLLu30sMB04espbPQQMAJSJ4ZG0wjCf45eIc9LIBN1pF4OAAYAyUVkRm9QRjFn0Hl8IGAAoE0vmNU7qCCYMnRa2NXirh4ABgDJRU53Q4nkNsjwOYsyklYubVZHgCAYAkIebr1k4YWiYSdWVcV1/9XyvtRAwAFBGGusqdc8dq1RbnVAifu4mPhEPVF9ToXvuWK3a6oTXWrhVDACUmebGan3i7jdp74ET2ryzUydODclkam6s0vq1c7RkfuOkTga4UAQMAJShWBBoxeJmrVjcXLQa6CIDAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHjBJ1oCwDThnNOJU8MaSaZVWRFXY12FzPx9dDIBAwBlLplMa9urXdqyq1PDybQCk8LQqbY6ofXr5mjdspmKxQrfoUXAAEAZGxxK6rs/adepgRGl0u6saX39I3pq0yHt3NOtu29doYpErKDLZgwGAMpUGDp9/2e71Xdq+JxwGZVKh+rqHdTDT+4t+PIJGAAoU/uP9Kmvf1jh2NlyRjrtdORYvzq7Bwq6fAIGAMrUph0dSqbCvNqmw1BbdnYWdPkEDACUqWM9g3m3dU46fKy/oMsnYACgTIUT9Y1dZPuJEDAAUKaqqyZ3onBdTaKgyydgAKBMvXnVLMVj+V1ImYgHunL17IIun+tgAKDEJFNpvfp6rzbv7FDvySFJUm11Qletnq21y2equjLatF++fJZe2NYhaeKur8BMKxY3FbROjmAAoIT09J3W1/5th5588YB6+obkXDRA3z+Y1LO/Pqyvfn+7DnaclCRVVyV0x41LJjyKiccDve8dyxQv8NX8BAwAlIhTAyP6zqPtGhxKjXn6cSrtlEyF+uHje85c07JsYZPec8sy1dUklIifvclPxAPNqK/Uh25doXmt9QWvly4yACgRz207ouFkesJ2qXSoXzx/QB+9c7UkafG8Rv3e3ZfrYMcp7TnQq6HhtGqq4lqxpFltM2u93fCSgAGAEjCSTKt9X7dcnmcSd/UOqqfvtJobqyVJZqaFbQ1a2Nbgscqz0UUGACXgyLF+BcEkjjSctP/ISX8F5YGAAYASMJLnLV9GpUOnZHJy7yk0AgYASkBVxeRupR+PmaoqC3v7/ckiYACgBMxrrZOUfxeZc9LSBTO81ZMPAgYASkAsFujNK2cplsc4jJm0oK1edTUVU1DZ+AgYACgR117epvraivMO9pukykRM77hu0dQVNg4CBgBKREUipnvuWK3W5hol4sE5HWaJeKD6ugrdc+dqNdRVFqXGbFwHAwAlpLoqrnvuWKWO4wPasqtTx7oH5ZzU1FCpq9bM1qK5Dd4unJwsAgYASoyZqW1Wne66qa7YpZwXXWQAAC8IGACAF3SRAUCJSybTan+9R3sOntBIMq26mgqtXdpS9PEYAgYAStjWVzr1zJbDMtNZt/Dfd/CEKhIxvfuWpZo7qzhjNXSRAUCJen7bET2z5bBS6fCcz4dJpkINnE7q+4/t1pFj/UWpj4ABgBLU1TOoF3ccVSp9/htaplKhfvTkHoVhnvf5LyACBgBK0JZdnUrnGRqpVKjXD/V5ruhcBAwAlBjnnF59vSfvDx9LpkJt233Mb1FjIGAAoMSkUqFcvumS0T+Y9FTN+AgYACgxsVigyQ6pxGNTv7knYACgxASBaWZTdd7tYzHTkvmNHisaGwEDACVow7o5SsTz3IQ76fIVs/wWNAYCBgBK0IpFTaqvrdBEF+rH49EHldVWJ6amsCwEDACUoFgs0IdvW6kZ9ZXjjq8k4oFWLGrSTRsWTHF1EW4VAwAlqqY6oXvfvVbtr3dr044OnRwYUWCmMHSaP6dOG9bN0cK24t2PjIABgBKWiAd60/JZWrdspoZH0kqmQlVVxpSIx4pdGgEDAOXAzFRVGVdV8T8p+QzGYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBfciwwAyoxzToc7+7Xt1WM60T+seBBoYVu9Ll/ZOqWfC0PAAEAZ6ek7rR8+vkeDQ0klU+GZ1zu6B/Tijg6tXTZTb7t2oYLA/y38CRgAKBO9fUP61o/bNZJMnzMtnXaSpF17j2twKKl337zU++fEMAYDAGXikaf3jhku2VJppzeOnNTuN3q910PAAEAZ6OodVG/fcF5tk6lQm3Yc9VwRAQMAZeGVvd1Kp8OJG2Z09w3p1MCIx4oIGAAoCycHRuQm0T4WmE4PpbzVIxEwAFAWKhOxSbV3TorH/UYAAQMAZWDxvEYlJhEYsZhpRn2lx4oIGAAoC0sXzFAsz2tbYjHTlatavV8LQ8AAQBkIAtPN1yxQPHb+zbop6k67YtVs/zV5XwIAYEqsWTpTb71qruIx01jXUMZjgWqqE/rI7atUXeX/Onuu5AeAMnL1mjla1NaoLTs71L6/R3LRvclqayq0Ye1srVk2UxWTPCHgQhEwAFBmZjZV67brl+idb1ms4WRa8cAUjwfebw2Ti4ABgDIVBKbqyuJt5hmDAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADAi3ixCwAAn1LpUG8cPqn+wRHFYoHaZtWqZUZ1scuaFggYAGUplQ717EuHtX13lyQpDCUzyTmnlhnVumn9As2fU1/kKssbXWQAyk4yldZ3f9Kul9qPaSQZaiQZKpUOlUyFSqWdOrsH9YPHd6t9X3exSy1rBAyAsvPoU/t0rGdQ6bQbt00q7fTYs2/oeO/pKaxseiFgAJSVA0dOau+hPrnxs+WMdBhq884O/0VNUwQMgLIxOJTUD3/xWt7tnZN27+9RMpn2WNX0RcAAKBvbXu1SOszj0CWLmal/MOmpoumNgAFQFpxzeumVzmKXgSwEDICyMDiUUjIVTvp9zjnV1SY8VAQCBkBZCEMnM5v0+1YuaVEiHvNQEQgYAGWhuiouN+nxF2n92tmeKgIBA6AsxGOBli1q0mQOYm64ah63jfGIgAFQNjasnaNYkN9mramhUuvXtXmuaHojYACUjdaWGr31yrmKx86/aauuiuvuW1dOUVXTFze7BFBWrl47RzVVCW3cfFCpVHjmzLIgMJlJc2fV6V3XL1F9bUWRKy1/BAyAsrN6aYtWLmnW/iN9OthxSslkWvW1FVp1WYsa6yqLXd60QcAAKEtBYLps/gxdNn9GsUuZthiDAQB4QcAAALwgYAAAXhAwAAAvCBgAgBcEDADACwIGAOAFAQMA8IKAAQB4QcAAALwgYAAAXnAvMgCXlGPdg+rsHlDonBrrKrWwrUFBMPmPQkbxETAALgn7Dp7Q01sO6WT/iGSSnFMQmILAdPWa2dqwro2gKTEEDICi27qrQ89sPaxU2p09IfP8he0dOtTZr/e/fTkhU0IYgwFQVAc7TuqZrUfODZcsqXSow52n9MzWQ1NYGS4WAQOgqJ7fdlSpdDhhu1Ta6dftXUqm0lNQFQqBgAFQNKcGRnSkqz/v9mbS7v29HitCIREwAIrmxKlhxYL8N0PJVKjuvtMeK0IhETAASoqJQf5SQcAAKJqmhkql8xh/GZWIB2qZUeWxIhQSAQOgaOpqKjSvtW5S71m+qNlTNSg0AgZAUV13xVzFYxNviuKxQFesalUizmarVPCbAlBU82fX68b18xSPjT+2Eo+ZFrTV661XzpvCynCxuJIfQNFdsWq2Guur9MyWQ+o9OSQzk1N0x5hEPND6tXN01ZrZXMVfYggYAJeEJfMatWReo473ntaxngGFodRQV6EFc+plRrCUIgIGwCVlZlO1ZjZVF7sMFABjMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeBEvdgG4dAyPpNQ/mFRgpvq6CsVj7H8AuHAEDHS0q18v7ujQ/sN9CgI78/qapS26eu0czaivLGJ1AJKptDqODyqZSqu6Mq7ZLbVn/a9eqgiYaW7rrk49s/WwUulQkpQO3ZlpO3Z3adfebr33bcu0sK2hWCUC01b/4Ihe2H5UO/d2K8oTk3NO8Vigq9a06qo1c5SIX7o9DZduZfBu9/6es8IlV+ikZCrUj57Yo+O9p6e4OmB66+kb0oMP79SO3V1KpUKNJEONJNNKpkKdHk7phe1H9e1HX9HwSLrYpY6LgJmmnHN6atPBccMlWyoV6rltR6agKgCSlEym9b2ftmtoOK2sToWzpNJOvX1DevjJPVNb3CQQMNPUoc5TGspzz8dJ2nfwhE4PJf0WBUCS1P56j5KpiXf+0qHT0a5+dfUMTkFVk0fATFNHuwbyOnoZFYsFOtZDNxkwFTbv7MgrYKQoZLbu6vRc0YUhYKapVCqUG+fQe2xO6TD/QAJwYZxzOnFyeBLtpY7jAx4runAEzDRVV1sxqbNPnJPqqis8VgRg1KT2/SSFk9tbnDIEzDS1YlHTpP4oq6vimtVc7bEiAJJkZqqumtwVJDMaqjxVc3GmNGCccxoaGtLAwIBSqdSZ13t7e7V9+3a99NJLeuWVVzQ8nP/hIS5MVWVcyxc1KZbHxVrxeKAN6+bI7NK/sAsoB1esnKVYLL//t0Q80FWrWz1XdGGm5ELLI0eO6OWXX9bIyMhZr9fU1Gh4eFjp9NlnM+3du1d1dXW69tprVV3NXrMvb792oTq6BnRqYOSsCyyzxWOBFs6p1+UrZk1xdcD0dfnKVm3a2amJOstMUe/CpXohtNcjmDAMtXHjRm3duvWccJGkwcHBc8JlVH9/v5544gkNDFyag1floLIiro/etVoL2xoUC+ysW0/EY4FiMdO65TP1nluWcfQCTKHa6oTuuukyxc9zFGMmVVTE9MF3rrhk/z/NTaIffv369W7z5s15t9+4caP6+/svpK4zYrGYbr/99ouaByZ2sn9YL792XN19QwoCU9vMWq1Z1qKqCu4mBBTL4c5Tevz5N9R3akTpMDrzMxaYZNK81jq98y2L1VhX3HsFmtkW59z6saZ523p0dHRcdLhIUjqd1tGjR9XW1laAqjCehrpKveXKecUuA0CWebPr9fH3rtOx7kHtP9KnZDKt6qqEli9qUn3tpX9Wp7eA2bVrV8Hm1d7eTsAAmLZaW2rU2lJT7DImzdsYzOBg4W5dcPo0V5ADQKnhOhgAgBclETCcqgwApcdbwNTW1hZsXitXrizYvAAAU8NbwKxataog84nFYpo7d25B5gUAmDreAqatrU319fUXNQ8z0w033FCgigAAU8nrGMwNN9ygurq6C3pvXV2dbrnllgt+PwCguLxeph0EgW6++WYdO3ZMO3fuPOu2LxUVFVq5cqUWLVqknp4eHTp0SKlUSlVVVbrssstUVXVp3h0UAJCfKbkPSGtrq1pbx7/bZ3Nzs5qbm6eiFADAFCmJ05QBAKWHgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeGHOufwbm3VJesNfOQCAErPIOTdrrAmTChgAAPJFFxkAwAsCBgDgBQEDAPCCgCkRZnafmblxHieKXNsDZnaowPN8t5l9y8x2m1loZhvHaXd9Zvkvm1nKzPafZ55rzewxM+s3s24z+7qZXdAHEZnZ4sy6/8SFvL+YzOx9ZvaSmQ2Z2Rtm9udmFpvkPBJmtuN868DM7jCzpzPr+6SZbTazt+W0ucLMfprV5mEzW5bT5rPn+dsfmvwawFSZkg8cQ0F9SFLuxjxVjEI8e5+kKyQ9L+l8H2/6dkk3SNosyUmqH6uRmc2VtFFSu6S7Jc2QdL+kR8zseudcWJiyL21mdpukf5P0NUn/VdKVkj6vaL39ySRm9ceSZp5nOZ+S9I+Zx18p2pm9QlJNVpvlkn4p6WVJH1O0PfqMpKfN7Arn3LFM069K+mnOImozrz08iZox1ZxzPErgIek+RRvQZcWuZYzaHpB0qMDzDLK+f0bSxjzaPSRp/zjt/l7SCUkzsl67MbNOP3AB9S3OvPcTxV7/k6z7JUlP5bz2F5JGJM3Jcx6XSRpQFArnrIPMujkt6dMTzOerY/xO5ksakvS3E7z3tzPLvrPY65TH+A+6yMpMVlfajWb271ndQV80s+qctm1m9qCZHTezYTPbbmb3jjHPJWb2TTPryLTbZ2ZfGKPdlWb2SzMbNLPXzOz3L/TncHkeUeTbTtJ7JP3YOXci671PSzog6b2TLjBPZnavmW3LdEcdz6zHtpw2H810WfWbWV+m6+lTWdM3mNnPM7/Hwcz6/9IF1LJA0VHEQzmTvikpIen2PGf1T5K+I+lX40z/XUmhpC9PMJ/rJD2X8zs5pOiI5v0TvPfjkjol/SyPelEkBEzpiZlZPOcx1u/xIUl7JH1A0d777ynaMEiSzKxW0lOKNir/Q1GX1A5J3zSzT2a1WyLpRUV7+5/JtP+czu0eaZD0rcxy3ytpk6R/MrNbshtlwu+BC/vRL0wmWJco2nDl2ilpjaflflLRxvsVRb+HP5V0m6SnzKwu0+Z6RevsKUW/gw9J+mdFXXjKtPuZpLSio9g7JP2lcrq381yvazNfz1oPzrnXJQ0qj/VgZh+TtF7n7067XlFX5EfMbG9mbGyPmf1hTru0oiOnXMOSlprZmF2jZjZf0i2S/tU5V47dw2WDMZjS0z7Gaz+WdFfOa4865/448/1jZuYk/aWZfd45t1vS70haLukW59zGTLufmNlsSX9tZl9zzqUVhUm1pDc7545kzf8bOcurl/SfnXNPSpKZPS3pVkn3SHoyq10685hKTZJMUu8Y03okrSz0AjOD5n+lqGvvI1mvtysad/hdSf+gaC/+hHPu01lvfyzr+1WK6v/vzrntWa8/kLPIfNbr6AkNY62H3qzpYzKzJkl/J+lPnHPHR0NyDHMzj/sV7bzsVRSc/2hmcefc6NHvq5LeYmYJ51wys4x6RUFoin7uo2PM/7cV7Rzn/g3iEsMRTOl5v6QNOY9Pj9HueznPv6Po931N5vmNkg5nhcuohyTN0m/2Zm+V9EhOuIxlcDRcJMk5NyzpNUkLsxs55+LOuf80wbwKzUYXf55phbZSUqukf81+0Tn3jKLbLd2UeWmTpCYze8jM7jKzGTnzeU3ROMVXMt1tC8ZaWJ7r9WLXw/2KwuJrE7QLFO1wfMo598/OuSecc3+gaFD+z8xsdFlfkDRP0pfNbJ6ZLZL0dUmjwTVe9+d/lPRSTuDiEkTAlJ6XnXObcx57xmjXOc7zeZmvzRp777Aja7oktejcs9bGMtZe8bDOfwbYVOlVtFEdaw+9SdFRTKGNLmu8ddwsSc65pxTt3S+Q9ENJXWb2uJldnpnep6g76IikL0k6YNEp2R+8gJpGf86x1sMMnWc9mNm1irro/kxSYyYIGzKTq81sRlZwdGe+/jxnNo9Jmi2pTZKcc7+S9IeKzuo7JGl/po5vKOo6O6ceM7tG0VEdRy8lgIApX7PHeX4487VH0pwx3jf62uhG4rh+E0olyTk3qGjjtXaMyWsk7fKw2NGN43jreHT9yjn3fefcTYrC7v2KNsA/HR1bc8792jn3QUXB8B8UHUV8z8zWTbKmnZmvZ60HM1us6PTh862H1ZJiik717s08tmWm/UPmeWPOcnKNBtCZIxPn3JcUHemtk7TQOfcORd1rL4x2m+X4uKLT8r91nlpxiSBgyteHc55/RNE/9ouZ509Jmm9mb81p91FJxxQNTEvRXudduWc+laCHJd1pZqMbwdEB9kXycy3Fq4qOGj+S/aKZvSWzzKdy3+Cc63fOPSLpK4pCpiVneso597yk/6Xof3f1ZApyzh1QFAofy5l0r6SkpJ+c5+0/VXQklf24JzPt/2Se92ee/zDz9bacedym6HT2juwXnXPDzrmdzrmDZvYmSe9Q1gkpo8ysQtH6fNQ513WeWnGJYJC/9FxhZmNd4LY554yaO8zsfkUBcY2iM8AezAzwS9Eg8R9J+oGZ/U9FXRQfk/RORX3nowPGn5F0p6Rnzezzis5MmyfpXc65c05pnoiZpSR9Y6Lxgkx//IbM0xZJoZndnXm+yTn3RqbdLP1mPGOhpJqsdrucc6N75fcr2pA+bGb/W9He9t8qCtzRDeLo3vzrkj7nnPtsHj/S1Tb2nRQeVnR9yVfM7CFFY1vzJP2NonGVr2eW95eKji6fVNQNNl/Sf5H0a+dcl5ndJemTkv49U1dtZvopSc9l1Z3XelU06P6ImX1F0rcVXWj555K+kL3hN7O/yNS/1Dn3RmbaWcGQWVeS9GrOWN6jmZ/nK5m/1X2KusFuVXRyyej750v6A0nPKupOvTpT3w+cc98eo/a7FB3F0T1WKop9IQ6P/B76zYWW4z1m5rS7UdKPFO1V9kj6oqTqnHm2KTqN9riif/Dtku4dY9lLFW2MRtvtk/T3WdMf0BgXWirqTtmY85qT9MBF/rz3ZbW7+TztPpszzzcpGhcYUNSl84Cklpw2azPv/f0J6luc5+/jXkVHDcOKusW+Kaktaz53KjoN+WimzUFFg+hzM9NXSvquonAZktSlaAN+7YWs10zbD2TVdEBRkMRy2nw2M8/FeayDcy42VTQ+80VFR3Ejmb+tj+a0mS3p8ay/q12S/puk+DjL+1FmHVYU+/+RR34PPg+mzJjZfYr2jpe7sQf/cR6Za1f+RtGHKA0Wux6glDEGA5ztJkVHZ4QLcJEYgwGyOOdyB8ABXCC6yAAAXtBFBgDwgoABAHhBwAAAvCBgAABeEDAAAC/+P5aUi723QZAuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "#from IPython.display import Javascript  # Restrict height of output cell.\n",
    "#display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 430})'''))\n",
    "\n",
    "model = GCN()\n",
    "criterion = torch.nn.CrossEntropyLoss()  # Define loss criterion.\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)  # Define optimizer.\n",
    "\n",
    "def train(data):\n",
    "    optimizer.zero_grad()  # Clear gradients.\n",
    "    out, h = model(data.x, data.edge_index)  # Perform a single forward pass.\n",
    "    loss = criterion(out[data.train_mask], data.y[data.train_mask])  # Compute the loss solely based on the training nodes.\n",
    "    loss.backward()  # Derive gradients.\n",
    "    optimizer.step()  # Update parameters based on gradients.\n",
    "    return loss, h\n",
    "\n",
    "for epoch in range(401):\n",
    "    loss, h = train(data)\n",
    "    if epoch % 10 == 0:\n",
    "        visualize(h, color=data.y, epoch=epoch, loss=loss)\n",
    "        time.sleep(0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NgcpV4rjAWy-"
   },
   "source": [
    "As one can see, our 3-layer GCN model manages to linearly separating the communities and classifying most of the nodes correctly.\n",
    "\n",
    "Note that we do not need to reimplement standard GNN architectures, the library provides implementations for the most popular ones, including the GCN Layer:\n",
    "\n",
    "`torch_geometric.nn.GCNConv`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: graph classification \n",
    "\n",
    "Let's now have a closer look at how to apply **Graph Neural Networks (GNNs) to the task of graph classification**.\n",
    "Graph classification refers to the problem of classifiying entire graphs (in contrast to nodes), given a **dataset of graphs**, based on some structural graph properties.\n",
    "Here, we want to embed entire graphs, and we want to embed those graphs in such a way so that they are linearly separable given a task at hand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screen Shot 2020-08-27 at 13.13.26.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most common task for graph classification is **molecular property prediction**, in which molecules are represented as graphs, and the task may be to infer whether a molecule inhibits HIV virus replication or not.\n",
    "\n",
    "The TU Dortmund University has collected a wide range of different graph classification datasets, known as the [**TUDatasets**](https://chrsmrrs.github.io/datasets/), which are also accessible via [`torch_geometric.datasets.TUDataset`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.TUDataset) in PyTorch Geometric.\n",
    "Let's load and inspect one of the smaller ones, the **MUTAG dataset**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset: MUTAG(188):\n",
      "====================\n",
      "Number of graphs: 188\n",
      "Number of features: 7\n",
      "Number of classes: 2\n",
      "\n",
      "Data(edge_index=[2, 38], x=[17, 7], edge_attr=[38, 4], y=[1])\n",
      "=============================================================\n",
      "Number of nodes: 17\n",
      "Number of edges: 38\n",
      "Average node degree: 2.24\n",
      "Contains isolated nodes: False\n",
      "Contains self-loops: False\n",
      "Is undirected: True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/antonio/anaconda3/envs/geometric_new/lib/python3.9/site-packages/torch_geometric/deprecation.py:12: UserWarning: 'contains_isolated_nodes' is deprecated, use 'has_isolated_nodes' instead\n",
      "  warnings.warn(out)\n",
      "/home/antonio/anaconda3/envs/geometric_new/lib/python3.9/site-packages/torch_geometric/deprecation.py:12: UserWarning: 'contains_self_loops' is deprecated, use 'has_self_loops' instead\n",
      "  warnings.warn(out)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch_geometric.datasets import TUDataset\n",
    "\n",
    "dataset = TUDataset(root='data/TUDataset', name='MUTAG')\n",
    "\n",
    "print()\n",
    "print(f'Dataset: {dataset}:')\n",
    "print('====================')\n",
    "print(f'Number of graphs: {len(dataset)}')\n",
    "print(f'Number of features: {dataset.num_features}')\n",
    "print(f'Number of classes: {dataset.num_classes}')\n",
    "\n",
    "data = dataset[0]  # Get the first graph object.\n",
    "\n",
    "print()\n",
    "print(data)\n",
    "print('=============================================================')\n",
    "\n",
    "# Gather some statistics about the first graph.\n",
    "print(f'Number of nodes: {data.num_nodes}')\n",
    "print(f'Number of edges: {data.num_edges}')\n",
    "print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')\n",
    "print(f'Contains isolated nodes: {data.contains_isolated_nodes()}')\n",
    "print(f'Contains self-loops: {data.contains_self_loops()}')\n",
    "print(f'Is undirected: {data.is_undirected()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(edge_index=[2, 38], x=[17, 7], edge_attr=[38, 4], y=[1])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset provides **188 different graphs**, and the task is to classify each graph into **one out of two classes**.\n",
    "\n",
    "By inspecting the first graph object of the dataset, we can see that it comes with **17 nodes (with 7-dimensional feature vectors)** and **38 edges** (leading to an average node degree of 2.24).\n",
    "It also comes with exactly **one graph label** (`y=[1]`), and, in addition to previous datasets, provides addtional **4-dimensional edge features** (`edge_attr=[38, 4]`).\n",
    "However, for the sake of simplicity, we will not make use of those.\n",
    "\n",
    "PyTorch Geometric provides some useful utilities for working with graph datasets, *e.g.*, we can shuffle the dataset and use the first 150 graphs as training graphs, while using the remaining ones for testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training graphs: 150\n",
      "Number of test graphs: 38\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(12345)\n",
    "dataset = dataset.shuffle()\n",
    "\n",
    "train_dataset = dataset[:150]\n",
    "test_dataset = dataset[150:]\n",
    "\n",
    "print(f'Number of training graphs: {len(train_dataset)}')\n",
    "print(f'Number of test graphs: {len(test_dataset)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mini-batching of graphs\n",
    "\n",
    "Since graphs in graph classification datasets are usually small, a good idea is to **batch the graphs** before inputting them into a Graph Neural Network to guarantee full GPU utilization.\n",
    "In the image or language domain, this procedure is typically achieved by **rescaling** or **padding** each example into a set of equally-sized shapes, and examples are then grouped in an additional dimension.\n",
    "The length of this dimension is then equal to the number of examples grouped in a mini-batch and is typically referred to as the `batch_size`.\n",
    "\n",
    "**NOT A GOOD IDEA**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, for GNNs the two approaches described above are either not feasible or may result in a lot of unnecessary memory consumption.\n",
    "Therefore, PyTorch Geometric opts for another approach to achieve parallelization across a number of examples. Here, adjacency matrices are stacked in a diagonal fashion (creating a giant graph that holds multiple isolated subgraphs), and node and target features are simply concatenated in the node dimension:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screen Shot 2020-08-27 at 13.11.53.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This procedure has some crucial advantages over other batching procedures:\n",
    "\n",
    "1. GNN operators that rely on a message passing scheme do not need to be modified since messages are not exchanged between two nodes that belong to different graphs.\n",
    "\n",
    "2. There is no computational or memory overhead since adjacency matrices are saved in a sparse fashion holding only non-zero entries, *i.e.*, the edges.\n",
    "\n",
    "PyTorch Geometric automatically takes care of **batching multiple graphs into a single giant graph** with the help of the [`torch_geometric.data.DataLoader`](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.DataLoader) class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1:\n",
      "=======\n",
      "Number of graphs in the current batch: 64\n",
      "DataBatch(edge_index=[2, 2492], x=[1135, 7], edge_attr=[2492, 4], y=[64], batch=[1135], ptr=[65])\n",
      "\n",
      "Step 2:\n",
      "=======\n",
      "Number of graphs in the current batch: 64\n",
      "DataBatch(edge_index=[2, 2592], x=[1168, 7], edge_attr=[2592, 4], y=[64], batch=[1168], ptr=[65])\n",
      "\n",
      "Step 3:\n",
      "=======\n",
      "Number of graphs in the current batch: 22\n",
      "DataBatch(edge_index=[2, 910], x=[411, 7], edge_attr=[910, 4], y=[22], batch=[411], ptr=[23])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from torch_geometric.data import DataLoader\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
    "\n",
    "for step, data in enumerate(train_loader):\n",
    "    print(f'Step {step + 1}:')\n",
    "    print('=======')\n",
    "    print(f'Number of graphs in the current batch: {data.num_graphs}')\n",
    "    print(data)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test batch_size = 147\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show adj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Graph Neural Network (GNN)\n",
    "\n",
    "Training a GNN for graph classification usually follows a simple recipe:\n",
    "\n",
    "1. Embed each node by performing multiple rounds of message passing\n",
    "2. Aggregate node embeddings into a unified graph embedding (**readout layer**)\n",
    "3. Train a final classifier on the graph embedding\n",
    "\n",
    "There exists multiple **readout layers** in literature, but the most common one is to simply take the average of node embeddings:\n",
    "\n",
    "$$\n",
    "\\mathbf{x}_{\\mathcal{G}} = \\frac{1}{|\\mathcal{V}|} \\sum_{v \\in \\mathcal{V}} \\mathcal{x}^{(L)}_v\n",
    "$$\n",
    "\n",
    "PyTorch Geometric provides this functionality via [`torch_geometric.nn.global_mean_pool`](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.glob.global_mean_pool), which takes in the node embeddings of all nodes in the mini-batch and the assignment vector `batch` to compute a graph embedding of size `[batch_size, hidden_channels]` for each graph in the batch.\n",
    "\n",
    "The final architecture for applying GNNs to the task of graph classification then looks as follows and allows for complete end-to-end training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): GCNConv(7, 64)\n",
      "  (conv2): GCNConv(64, 64)\n",
      "  (conv3): GCNConv(64, 64)\n",
      "  (lin): Linear(in_features=64, out_features=2, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from torch.nn import Linear\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GCNConv\n",
    "from torch_geometric.nn import global_mean_pool\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345678)\n",
    "        self.conv1 = GCNConv(dataset.num_node_features, hidden_channels)\n",
    "        self.conv2 = GCNConv(hidden_channels, hidden_channels)\n",
    "        self.conv3 = GCNConv(hidden_channels, hidden_channels)\n",
    "        self.lin = Linear(hidden_channels, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index, batch):\n",
    "        # 1. Obtain node embeddings \n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv3(x, edge_index)\n",
    "        \n",
    "        # 2. Readout layer\n",
    "        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]\n",
    "        \n",
    "        # 3. Apply a final classifier\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = self.lin(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "model = GCN(hidden_channels=64)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we again make use of the [`GCNConv`](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GCNConv) with $\\mathrm{ReLU}(x) = \\max(x, 0)$ activation for obtaining localized node embeddings, before we apply our final classifier on top of a graph readout layer.\n",
    "\n",
    "Let's train our network for a few epochs to see how well it performs on the training as well as test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 010, Train Acc: 0.6533, Test Acc: 0.7368\n",
      "Epoch: 020, Train Acc: 0.7533, Test Acc: 0.7632\n",
      "Epoch: 030, Train Acc: 0.7600, Test Acc: 0.7632\n",
      "Epoch: 040, Train Acc: 0.7600, Test Acc: 0.7632\n",
      "Epoch: 050, Train Acc: 0.7667, Test Acc: 0.7632\n"
     ]
    }
   ],
   "source": [
    "#from IPython.display import Javascript\n",
    "#display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 300})'''))\n",
    "\n",
    "model = GCN(hidden_channels=64)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "def train():\n",
    "    model.train()\n",
    "\n",
    "    for data in train_loader:  # Iterate in batches over the training dataset.\n",
    "         out = model(data.x, data.edge_index, data.batch)  # Perform a single forward pass.\n",
    "         loss = criterion(out, data.y)  # Compute the loss.\n",
    "         loss.backward()  # Derive gradients.\n",
    "         optimizer.step()  # Update parameters based on gradients.\n",
    "         optimizer.zero_grad()  # Clear gradients.\n",
    "\n",
    "def test(loader):\n",
    "     model.eval()\n",
    "\n",
    "     correct = 0\n",
    "     for data in loader:  # Iterate in batches over the training/test dataset.\n",
    "         out = model(data.x, data.edge_index, data.batch)  \n",
    "         pred = out.argmax(dim=1)  # Use the class with highest probability.\n",
    "         correct += int((pred == data.y).sum())  # Check against ground-truth labels.\n",
    "     return correct / len(loader.dataset)  # Derive ratio of correct predictions.\n",
    "\n",
    "\n",
    "for epoch in range(1, 51):\n",
    "    train()\n",
    "    train_acc = test(train_loader)\n",
    "    test_acc = test(test_loader)\n",
    "    if epoch % 10 == 0:\n",
    "        print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one can see, our model reaches around **76% test accuracy**.\n",
    "Reasons for the fluctations in accuracy can be explained by the rather small dataset (only 38 test graphs), and usually disappear once one applies GNNs to larger datasets.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other datasets?\n",
    "\n",
    "\n",
    "Doc https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html  \n",
    "OGB https://ogb.stanford.edu/docs/dataset_overview/\n",
    "\n",
    "# Other architectures?\n",
    "\n",
    "Doc https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#convolutional-layers\n",
    "\n",
    "# Other resources?\n",
    "Doc https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# let's move higher order [super fast]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): GraphConv(7, 64)\n",
      "  (conv2): GraphConv(64, 64)\n",
      "  (lin): Linear(in_features=64, out_features=2, bias=True)\n",
      ")\n",
      "Epoch: 010, Train Acc: 0.6533, Test Acc: 0.7368\n",
      "Epoch: 020, Train Acc: 0.7067, Test Acc: 0.7895\n",
      "Epoch: 030, Train Acc: 0.7400, Test Acc: 0.8158\n",
      "Epoch: 040, Train Acc: 0.7267, Test Acc: 0.8158\n",
      "Epoch: 050, Train Acc: 0.7400, Test Acc: 0.8158\n"
     ]
    }
   ],
   "source": [
    "from torch.nn import Linear\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GraphConv, GINConv, GINEConv, GATConv # ......\n",
    "from torch_geometric.nn import global_mean_pool\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345678)\n",
    "        self.conv1 = GraphConv(dataset.num_node_features, hidden_channels)\n",
    "        self.conv2 = GraphConv(hidden_channels, hidden_channels)\n",
    "        self.lin = Linear(hidden_channels, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index, batch):\n",
    "        # 1. Obtain node embeddings \n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv2(x, edge_index)\n",
    "        \n",
    "        # 2. Readout layer\n",
    "        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]\n",
    "        \n",
    "        # 3. Apply a final classifier\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = self.lin(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "model = GCN(hidden_channels=64)\n",
    "print(model)\n",
    "\n",
    "\n",
    "#from IPython.display import Javascript\n",
    "#display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 300})'''))\n",
    "\n",
    "model = GCN(hidden_channels=64)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "def train():\n",
    "    model.train()\n",
    "\n",
    "    for data in train_loader:  # Iterate in batches over the training dataset.\n",
    "         out = model(data.x, data.edge_index, data.batch)  # Perform a single forward pass.\n",
    "         loss = criterion(out, data.y)  # Compute the loss.\n",
    "         loss.backward()  # Derive gradients.\n",
    "         optimizer.step()  # Update parameters based on gradients.\n",
    "         optimizer.zero_grad()  # Clear gradients.\n",
    "\n",
    "def test(loader):\n",
    "     model.eval()\n",
    "\n",
    "     correct = 0\n",
    "     for data in loader:  # Iterate in batches over the training/test dataset.\n",
    "         out = model(data.x, data.edge_index, data.batch)  \n",
    "         pred = out.argmax(dim=1)  # Use the class with highest probability.\n",
    "         correct += int((pred == data.y).sum())  # Check against ground-truth labels.\n",
    "     return correct / len(loader.dataset)  # Derive ratio of correct predictions.\n",
    "\n",
    "\n",
    "for epoch in range(1, 51):\n",
    "    train()\n",
    "    train_acc = test(train_loader)\n",
    "    test_acc = test(test_loader)\n",
    "    if epoch % 10 == 0:\n",
    "        print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "1. Introduction.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}