{ "source_file": "0930", "source_platform": "Medium", "author": "Maxime Labonne", "title_listed": "Graph Convolutional Networks: Introduction to GNNs", "keywords": "GNN,图神经网络,PyTorch Geometric", "domain": "机器学习", "content_type": "深度技术解析", "is_paid_flag": "部分免费/会员墙", "url": "https://towardsdatascience.com/graph-convolutional-networks-introduction-to-gnns-24b3f60d6c95/", "publish_date": "2023-08", "corpus_stage": "预训练/SFT", "notes": "", "id": "83deaecba8a3", "collected_at": "2026-10-01T21:53:55", "title": "Graph Convolutional Networks: Introduction to GNNs | Towards Data Science", "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Graph Convolutional Networks: Introduction to GNNs\nA step-by-step guide using PyTorch Geometric\n[Maxime Labonne](https://towardsdatascience.com/author/mlabonne/)\nAugust 14, 202317 min read\n![Image by author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1X4I4_PO9l14apt0h3Nbw2A.jpeg)\nImage by author\n**Graph Neural Networks** (GNNs) represent one of the most captivating and rapidly evolving architectures within the deep learning landscape. As deep learning models designed to process data structured as graphs, GNNs bring remarkable versatility and powerful learning capabilities.\nAmong the various types of GNNs, the **Graph Convolutional Networks** (GCNs) have emerged as the most [prevalent and broadly applied model](https://paperswithcode.com/methods/category/graph-models). GCNs are innovative due to their ability to leverage both the features of a node and its locality to make predictions, providing an effective way to handle graph-structured data.\nIn this article, we will delve into the mechanics of the GCN layer and explain its inner workings. Furthermore, we will explore its practical application for node classification tasks, using [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/index.html) as our tool of choice.\n[PyTorch](https://pytorch.org/get-started/locally/) Geometric is a specialized extension of PyTorch that has been created specifically for the development and implementation of GNNs. It is an advanced, yet user-friendly library that provides a comprehensive suite of tools to facilitate graph-based machine learning. To commence our journey, the PyTorch Geometric installation will be required. If you are using Google Colab, PyTorch should already be in place, so all we need to do is execute a few additional commands.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\nAll the code is available on [Google Colab](https://colab.research.google.com/drive/1ZugveUjRrbSNwUbryeKJN2wyhGFRCw0q?usp=sharing) and [GitHub](https://github.com/mlabonne/graph-neural-network-course).\npython\n\n```\n!pip install torch_geometric\n```\n\ntext\n\n```\nimport torch\nimport numpy as np\nimport networkx as nx\nimport matplotlib.pyplot as plt\n```\n\nNow that PyTorch Geometric is installed, let's explore the dataset we will use in this tutorial.\n## 🌐 I. Graph data\n[Graphs](https://en.wikipedia.org/wiki/Graph_\\(discrete_mathematics\\)) are an essential structure for representing relationships between objects. You can encounter graph data in a multitude of real-world scenarios, such as social and computer networks, chemical structures of molecules, natural language processing, and image recognition, to name a few.\nIn this article, we will study the infamous and much-used [Zachary's karate club](https://en.wikipedia.org/wiki/Zachary%27s_karate_club) dataset.\n![Image by author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1gPUxh53IHnS0lnnks8_L3Q.png)\nImage by author\nThe Zachary's karate club dataset embodies the relationships formed within a karate club as observed by Wayne W. Zachary during the 1970s. It is a kind of social network, where each node represents a club member, and edges between nodes represent interactions that occurred outside the club environment.\nIn this particular scenario, the members of the club are split into four distinct groups. Our task is to **assign the correct group to each member** (node classification), based on the pattern of their interactions.\nLet's import the dataset with PyG's built-in function and try to understand the `Datasets` object it uses.\njavascript\n\n```\nfrom torch_geometric.datasets import KarateClub\n```\n\ntext\n\n```\n# Import dataset from PyTorch Geometric\ndataset = KarateClub()\n```\n\ntext\n\n```\n# Print information\nprint(dataset)\nprint('------------')\nprint(f'Number of graphs: {len(dataset)}')\nprint(f'Number of features: {dataset.num_features}')\nprint(f'Number of classes: {dataset.num_classes}')\n```\n\ntext\n\n```\nKarateClub()\n------------\nNumber of graphs: 1\nNumber of features: 34\nNumber of classes: 4\n```\n\nThis dataset only has 1 graph, where each node has a feature vector of 34 dimensions and is part of one out of four classes (our four groups). Actually, the `Datasets` object can be seen as a collection of `Data` (graph) objects.\nWe can further inspect our unique graph to know more about it.\npython\n\n```\n# Print first element\nprint(f'Graph: {dataset[0]}')\n```\n\ncss\n\n```\nGraph: Data(x=[34, 34], edge_index=[2, 156], y=[34], train_mask=[34])\n```\n\nThe `[Data](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html)` object is particularly interesting. Printing it offers a good summary of the graph we're studying:\n * `x=[34, 34]` is the **node feature matrix** with shape (number of nodes, number of features). In our case, it means that we have 34 nodes (our 34 members), each node being associated to a 34-dim feature vector.\n * `edge_index=[2, 156]` represents the **graph connectivity** (how the nodes are connected) with shape (2, number of directed edges).\n * `y=[34]` is the **node ground-truth labels**. In this problem, every node is assigned to one class (group), so we have one value for each node.\n * `train_mask=[34]` is an optional attribute that tells which nodes should be used for training with a list of `True` or `False` statements.\n\n\nLet's print each of these tensors to understand what they store. Let's start with the node features.\nini\n\n```\ndata = dataset[0]\n```\n\ntext\n\n```\nprint(f'x = {data.x.shape}')\nprint(data.x)\n```\n\ntext\n\n```\nx = torch.Size([34, 34])\ntensor([[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.]])\n```\n\nHere, the node feature matrix `x` is an identity matrix: it **doesn't contain any relevant information** about the nodes. It could contain information like age, skill level, etc. but this is not the case in this dataset. It means we'll have to classify our nodes just by looking at their connections.\nNow, let's print the edge index.\npython\n\n```\nprint(f'edge_index = {data.edge_index.shape}')\nprint(data.edge_index)\n```\n\ntext\n\n```\nedge_index = torch.Size([2, 156])\ntensor([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n 3, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7,\n 7, 7, 8, 8, 8, 8, 8, 9, 9, 10, 10, 10, 11, 12, 12, 13, 13, 13,\n 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 19, 20, 20, 21,\n 21, 22, 22, 23, 23, 23, 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, 27, 27,\n 27, 27, 28, 28, 28, 29, 29, 29, 29, 30, 30, 30, 30, 31, 31, 31, 31, 31,\n 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33,\n 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33],\n [ 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 17, 19, 21, 31, 0, 2,\n 3, 7, 13, 17, 19, 21, 30, 0, 1, 3, 7, 8, 9, 13, 27, 28, 32, 0,\n 1, 2, 7, 12, 13, 0, 6, 10, 0, 6, 10, 16, 0, 4, 5, 16, 0, 1,\n 2, 3, 0, 2, 30, 32, 33, 2, 33, 0, 4, 5, 0, 0, 3, 0, 1, 2,\n 3, 33, 32, 33, 32, 33, 5, 6, 0, 1, 32, 33, 0, 1, 33, 32, 33, 0,\n 1, 32, 33, 25, 27, 29, 32, 33, 25, 27, 31, 23, 24, 31, 29, 33, 2, 23,\n 24, 33, 2, 31, 33, 23, 26, 32, 33, 1, 8, 32, 33, 0, 24, 25, 28, 32,\n 33, 2, 8, 14, 15, 18, 20, 22, 23, 29, 30, 31, 33, 8, 9, 13, 14, 15,\n 18, 19, 20, 22, 23, 26, 27, 28, 29, 30, 31, 32]])\n```\n\nIn graph theory and network analysis, connectivity between nodes is stored using a variety of data structures. The `edge_index` is one such data structure, where the graph's connections are stored in **two lists** (156 directed edges, which equate to 78 bidirectional edges). The reason for these two lists is that one list stores the source nodes, while the second one identifies the destination nodes.\nThis method is known as a **coordinate list** (COO) format, which is essentially a means to efficiently store a [sparse matrix](https://en.wikipedia.org/wiki/Sparse_matrix#Storing_a_sparse_matrix). Sparse matrices are data structures that efficiently store matrices with a majority of zero elements. In the COO format, only non-zero elements are stored, saving memory and computational resources.\nContrarily, a more intuitive and straightforward way to represent graph connectivity is through an **adjacency matrix** _A_. This is a square matrix where each element _A_ᵢⱼ _s_pecifies the presence or absence of an edge from node _i_ to node _j_ in the graph. In other words, a non-zero element _A_ᵢⱼ implies a connection from node _i_ to node _j_ , and a zero indicates no direct connection.\n![Image by author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1CjPY3V96hZ647kb38hVaNg.png)\nImage by author\nAn adjacency matrix, however, is not as space-efficient as the COO format for sparse matrices or graphs with fewer edges. However, for clarity and easy interpretation, the adjacency matrix remains a popular choice for representing graph connectivity.\nThe adjacency matrix can be inferred from the `edge_index` with a utility function `to_dense_adj()`.\njavascript\n\n```\nfrom torch_geometric.utils import to_dense_adj\n```\n\ntext\n\n```\nA = to_dense_adj(data.edge_index)[0].numpy().astype(int)\nprint(f'A = {A.shape}')\nprint(A)\n```\n\ntext\n\n```\nA = (34, 34)\n[[0 1 1 ... 1 0 0]\n [1 0 1 ... 0 0 0]\n [1 1 0 ... 0 1 0]\n ...\n [1 0 0 ... 0 1 1]\n [0 0 1 ... 1 0 1]\n [0 0 0 ... 1 1 0]]\n```\n\nWith graph data, it is relatively uncommon for nodes to be densely interconnected. As you can see, our adjacency matrix _A_ is **sparse** (filled with zeros).\nIn many real-world graphs, most nodes are connected to only a few other nodes, resulting in a large number of zeros in the adjacency matrix. Storing so many zeros is not efficient at all, which is why the COO format is adopted by PyG.\nOn the contrary, ground-truth labels are easy to understand.\npython\n\n```\nprint(f'y = {data.y.shape}')\nprint(data.y)\n```\n\ntext\n\n```\ny = torch.Size([34])\ntensor([1, 1, 1, 1, 3, 3, 3, 1, 0, 1, 3, 1, 1, 1, 0, 0, 3, 1, 0, 1, 0, 1, 0, 0,\n 2, 2, 0, 0, 2, 0, 0, 2, 0, 0])\n```\n\nOur node ground-truth labels stored in `y` simply encode the group number (0, 1, 2, 3) for each node, which is why we have 34 values.\nFinally, let's print the train mask.\npython\n\n```\nprint(f'train_mask = {data.train_mask.shape}')\nprint(data.train_mask)\n```\n\ntext\n\n```\ntrain_mask = torch.Size([34])\ntensor([ True, False, False, False, True, False, False, False, True, False,\n False, False, False, False, False, False, False, False, False, False,\n False, False, False, False, True, False, False, False, False, False,\n False, False, False, False])\n```\n\nThe train mask shows which nodes are supposed to be used for training with `True` statements. These nodes represent the training set, while the others can be considered as the test set. This division helps in model evaluation by providing unseen data for testing.\nBut we're not done yet! The `[Data](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html)` object has a lot more to offer. It provides various utility functions that enable the investigation of several properties of the graph. For instance:\n * `is_directed()` tells you if the graph is **directed**. A directed graph signifies that the adjacency matrix is not symmetric, i.e., the direction of edges matters in the connections between nodes.\n * `isolated_nodes()` checks if some nodes are **not connected** to the rest of the graph. These nodes are likely to pose challenges in tasks like classification due to their lack of connections.\n * `has_self_[loops](https://en.wikipedia.org/wiki/Loop_(graph_theory))()` indicates if at least one node is **connected to itself**. This is distinct from the concept of loops: a loop implies a path that starts and ends at the same node, traversing other nodes in between.\n\n\nIn the context of the Zachary's karate club dataset, all these properties return `False`. This implies that the graph is not directed, does not have any isolated nodes, and none of its nodes are connected to themselves.\npython\n\n```\nprint(f'Edges are directed: {data.is_directed()}')\nprint(f'Graph has isolated nodes: {data.has_isolated_nodes()}')\nprint(f'Graph has loops: {data.has_self_loops()}')\n```\n\ntext\n\n```\nEdges are directed: False\nGraph has isolated nodes: False\nGraph has loops: False\n```\n\nFinally, we can convert a graph from PyTorch Geometric to the popular graph library [NetworkX](https://networkx.org/) using `[to_networkx](https://pytorch-geometric.readthedocs.io/en/latest/modules/utils.html?highlight=to_networkx#torch_geometric.utils.to_networkx)`. This is particularly useful to visualize a small graph with `networkx` and `matplotlib`.\nLet's plot our dataset with a different color for each group.\njavascript\n\n```\nfrom torch_geometric.utils import to_networkx\n```\n\ntext\n\n```\nG = to_networkx(data, to_undirected=True)\nplt.figure(figsize=(12,12))\nplt.axis('off')\nnx.draw_networkx(G,\n pos=nx.spring_layout(G, seed=0),\n with_labels=True,\n node_size=800,\n node_color=data.y,\n cmap=\"hsv\",\n vmin=-2,\n vmax=3,\n width=0.8,\n edge_color=\"grey\",\n font_size=14\n )\nplt.show()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1hPouqFUjF4BwzTJeJWyNvQ.png)\nThis plot of Zachary's karate club displays our 34 nodes, 78 (bidirectional) edges, and 4 labels with 4 different colors. Now that we've seen the essentials of loading and handling a dataset with PyTorch Geometric, we can introduce the **Graph Convolutional Network** architecture.\n## ✉️ II. Graph Convolutional Network\nThis section aims to introduce and build the graph convolutional layer from the ground up.\nIn traditional neural networks, linear layers apply a **linear transformation** to the incoming data. This transformation converts input features _x_ into hidden vectors _h_ through the use of a weight matrix 𝐖. Ignoring biases for the time being, this can be expressed as:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1oJzwGLdNLaJrjA-bufR-BQ.png)\nWith graph data, an additional layer of complexity is added through the **connections between nodes**. These connections matter because, typically, in networks, it's assumed that similar nodes are more likely to be linked to each other than dissimilar ones, a phenomenon known as [network homophily](https://en.wikipedia.org/wiki/Network_homophily).\nWe can enrich our **node representation** by merging its features with those of its neighbors. This operation is called convolution, or neighborhood aggregation. Let's represent the neighborhood of node _i_ including itself as _Ñ_.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1fiqRdbHBbKeqoS49B6ruyg.png)\nUnlike filters in Convolutional Neural Networks (CNNs), our weight matrix 𝐖 is unique and shared among every node. But there is another issue: nodes do not have a f**ixed number of neighbors** like pixels do.\nHow do we address cases where one node has only one neighbor, and another has 500? If we simply sum the feature vectors, the resulting embedding _h_ would be much larger for the node with 500 neighbors. To ensure a **similar range** of values for all nodes and comparability between them, we can normalize the result based on the **degree** of nodes, where degree refers to the number of connections a node has.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/17uGB3skMU0d5qGLrCd70MA.png)\nWe're almost there! Introduced by Kipf et al. (2016), the [graph convolutional layer](https://arxiv.org/abs/1609.02907) has one final improvement.\nThe authors observed that features from nodes with numerous neighbors propagate much more easily than those from more isolated nodes. To offset this effect, they suggested assigning **bigger weights** to features from nodes with fewer neighbors, thus balancing the influence across all nodes. This operation is written as:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1BNyzJMlREISPhvrKn39Ptw.png)\nNote that when _i_ and _j_ have the same number of neighbors, it is equivalent to our own layer. Now, let's see how to implement it in Python with PyTorch Geometric.\n## 🧠 III. Implementing a GCN\nPyTorch Geometric provides the `GCNConv` function, which directly implements the graph convolutional layer.\nIn this example, we'll create a basic Graph Convolutional Network with a single GCN layer, a ReLU activation function, and a linear output layer. This output layer will yield **four values** corresponding to our four categories, with the highest value determining the class of each node.\nIn the following code block, we define the GCN layer with a 3-dimensional hidden layer.\njavascript\n\n```\nfrom torch.nn import Linear\nfrom torch_geometric.nn import GCNConv\n\n```\n\ntext\n\n```\nclass GCN(torch.nn.Module):\n def __init__(self):\n super().__init__()\n self.gcn = GCNConv(dataset.num_features, 3)\n self.out = Linear(3, dataset.num_classes)\n```\n\ntext\n\n```\n def forward(self, x, edge_index):\n h = self.gcn(x, edge_index).relu()\n z = self.out(h)\n return h, z\n```\n\ntext\n\n```\nmodel = GCN()\nprint(model)\n```\n\ntext\n\n```\nGCN(\n (gcn): GCNConv(34, 3)\n (out): Linear(in_features=3, out_features=4, bias=True)\n)\n```\n\nIf we added a second GCN layer, our model would not only aggregate feature vectors from the neighbors of each node, but also from the neighbors of these neighbors.\nWe can **stack several graph layers** to aggregate more and more distant values, but there's a catch: if we add too many layers, the aggregation becomes so intense that all the embeddings end up looking the same. This phenomenon is called **over-smoothing** and can be a real problem when you have too many layers.\nNow that we've defined our GNN, let's write a simple training loop with PyTorch. I chose a regular cross-entropy loss since it's a multi-class classification task, with Adam as optimizer. In this article, we won't implement a train/test split to keep things simple and focus on how GNNs learn instead.\nThe training loop is standard: we try to predict the correct labels, and we compare the GCN's results to the values stored in `data.y`. The error is calculated by the cross-entropy loss and backpropagated with Adam to fine-tune our GNN's weights and biases. Finally, we print metrics every 10 epochs.\nini\n\n```\ncriterion = torch.nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.02)\n```\n\ntext\n\n```\n# Calculate accuracy\ndef accuracy(pred_y, y):\n return (pred_y == y).sum() / len(y)\n```\n\ntext\n\n```\n# Data for animations\nembeddings = []\nlosses = []\naccuracies = []\noutputs = []\n```\n\ntext\n\n```\n# Training loop\nfor epoch in range(201):\n # Clear gradients\n optimizer.zero_grad()\n```\n\ntext\n\n```\n # Forward pass\n h, z = model(data.x, data.edge_index)\n```\n\ntext\n\n```\n # Calculate loss function\n loss = criterion(z, data.y)\n```\n\ntext\n\n```\n # Calculate accuracy\n acc = accuracy(z.argmax(dim=1), data.y)\n```\n\ntext\n\n```\n # Compute gradients\n loss.backward()\n```\n\ntext\n\n```\n # Tune parameters\n optimizer.step()\n```\n\ntext\n\n```\n # Store data for animations\n embeddings.append(h)\n losses.append(loss)\n accuracies.append(acc)\n outputs.append(z.argmax(dim=1))\n```\n\ntext\n\n```\n # Print metrics every 10 epochs\n if epoch % 10 == 0:\n print(f'Epoch {epoch:>3} | Loss: {loss:.2f} | Acc: {acc*100:.2f}%')\n```\n\ntext\n\n```\nEpoch 0 | Loss: 1.40 | Acc: 41.18%\nEpoch 10 | Loss: 1.21 | Acc: 47.06%\nEpoch 20 | Loss: 1.02 | Acc: 67.65%\nEpoch 30 | Loss: 0.80 | Acc: 73.53%\nEpoch 40 | Loss: 0.59 | Acc: 73.53%\nEpoch 50 | Loss: 0.39 | Acc: 94.12%\nEpoch 60 | Loss: 0.23 | Acc: 97.06%\nEpoch 70 | Loss: 0.13 | Acc: 100.00%\nEpoch 80 | Loss: 0.07 | Acc: 100.00%\nEpoch 90 | Loss: 0.05 | Acc: 100.00%\nEpoch 100 | Loss: 0.03 | Acc: 100.00%\nEpoch 110 | Loss: 0.02 | Acc: 100.00%\nEpoch 120 | Loss: 0.02 | Acc: 100.00%\nEpoch 130 | Loss: 0.02 | Acc: 100.00%\nEpoch 140 | Loss: 0.01 | Acc: 100.00%\nEpoch 150 | Loss: 0.01 | Acc: 100.00%\nEpoch 160 | Loss: 0.01 | Acc: 100.00%\nEpoch 170 | Loss: 0.01 | Acc: 100.00%\nEpoch 180 | Loss: 0.01 | Acc: 100.00%\nEpoch 190 | Loss: 0.01 | Acc: 100.00%\nEpoch 200 | Loss: 0.01 | Acc: 100.00%\n```\n\nGreat! Without much surprise, we reach 100% accuracy on the training set (full dataset). It means that our model learned to correctly assign every member of the karate club to its correct group.\nWe can produce a neat visualization by animating the graph and see the evolution of the GNN's predictions during the training process.\njavascript\n\n```\n%%capture\nfrom IPython.display import HTML\nfrom matplotlib import animation\nplt.rcParams[\"animation.bitrate\"] = 3000\n```\n\ntext\n\n```\ndef animate(i):\n G = to_networkx(data, to_undirected=True)\n nx.draw_networkx(G,\n pos=nx.spring_layout(G, seed=0),\n with_labels=True,\n node_size=800,\n node_color=outputs[i],\n cmap=\"hsv\",\n vmin=-2,\n vmax=3,\n width=0.8,\n edge_color=\"grey\",\n font_size=14\n )\n plt.title(f'Epoch {i} | Loss: {losses[i]:.2f} | Acc: {accuracies[i]*100:.2f}%',\n fontsize=18, pad=20)\n```\n\ntext\n\n```\nfig = plt.figure(figsize=(12, 12))\nplt.axis('off')\n```\n\ntext\n\n```\nanim = animation.FuncAnimation(fig, animate, \n np.arange(0, 200, 10), interval=500, repeat=True)\nhtml = HTML(anim.to_html5_video())\ndisplay(html)\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1SICganhibeJlFogOCFob1Q.gif)\nThe first predictions are random, but the GCN perfectly labels every node after a while. Indeed, the final graph is the same as the one we plotted at the end of the first section. But what does the GCN really learn?\nBy aggregating features from neighboring nodes, the GNN learns a vector representation (or **embedding**) of every node in the network. In our model, the final layer just learns how to use these representations to produce the best classifications. However, embeddings are the real products of GNNs.\nLet's print the embeddings learned by our model.\npython\n\n```\n# Print embeddings\nprint(f'Final embeddings = {h.shape}')\nprint(h)\n```\n\ntext\n\n```\nFinal embeddings = torch.Size([34, 3])\ntensor([[1.9099e+00, 2.3584e+00, 7.4027e-01],\n [2.6203e+00, 2.7997e+00, 0.0000e+00],\n [2.2567e+00, 2.2962e+00, 6.4663e-01],\n [2.0802e+00, 2.8785e+00, 0.0000e+00],\n [0.0000e+00, 0.0000e+00, 2.9694e+00],\n [0.0000e+00, 0.0000e+00, 3.3817e+00],\n [0.0000e+00, 1.5008e-04, 3.4246e+00],\n [1.7593e+00, 2.4292e+00, 2.4551e-01],\n [1.9757e+00, 6.1032e-01, 1.8986e+00],\n [1.7770e+00, 1.9950e+00, 6.7018e-01],\n [0.0000e+00, 1.1683e-04, 2.9738e+00],\n [1.8988e+00, 2.0512e+00, 2.6225e-01],\n [1.7081e+00, 2.3618e+00, 1.9609e-01],\n [1.8303e+00, 2.1591e+00, 3.5906e-01],\n [2.0755e+00, 2.7468e-01, 1.9804e+00],\n [1.9676e+00, 3.7185e-01, 2.0011e+00],\n [0.0000e+00, 0.0000e+00, 3.4787e+00],\n [1.6945e+00, 2.0350e+00, 1.9789e-01],\n [1.9808e+00, 3.2633e-01, 2.1349e+00],\n [1.7846e+00, 1.9585e+00, 4.8021e-01],\n [2.0420e+00, 2.7512e-01, 1.9810e+00],\n [1.7665e+00, 2.1357e+00, 4.0325e-01],\n [1.9870e+00, 3.3886e-01, 2.0421e+00],\n [2.0614e+00, 5.1042e-01, 2.4872e+00],\n...\n [2.1778e+00, 4.4730e-01, 2.0077e+00],\n [3.8906e-02, 2.3443e+00, 1.9195e+00],\n [3.0748e+00, 0.0000e+00, 3.0789e+00],\n [3.4316e+00, 1.9716e-01, 2.5231e+00]], grad_fn=)\n```\n\nAs you can see, embeddings do not need to have the same dimensions as feature vectors. Here, I chose to reduce the number of dimensions from 34 (`dataset.num_features`) to three to get a nice visualization in 3D.\nLet's plot these embeddings before any training happens, at epoch 0.\npython\n\n```\n# Get first embedding at epoch = 0\nembed = h.detach().cpu().numpy()\n```\n\ntext\n\n```\nfig = plt.figure(figsize=(12, 12))\nax = fig.add_subplot(projection='3d')\nax.patch.set_alpha(0)\nplt.tick_params(left=False,\n bottom=False,\n labelleft=False,\n labelbottom=False)\nax.scatter(embed[:, 0], embed[:, 1], embed[:, 2],\n s=200, c=data.y, cmap=\"hsv\", vmin=-2, vmax=3)\n```\n\ntext\n\n```\nplt.show()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1EP37HkRCQB0TitIlWARXgw.png)\nWe see every node from Zachary's karate club with their true labels (and not the model's predictions). For now, they're all over the place since the GNN is not trained yet. But if we plot these embeddings at each step of the training loop, we'd be able to visualize what the GNN truly learns.\nLet's see how they evolve over time, as the GCN gets better and better at classifying nodes.\npython\n\n```\n%%capture\n```\n\ntext\n\n```\ndef animate(i):\n embed = embeddings[i].detach().cpu().numpy()\n ax.clear()\n ax.scatter(embed[:, 0], embed[:, 1], embed[:, 2],\n s=200, c=data.y, cmap=\"hsv\", vmin=-2, vmax=3)\n plt.title(f'Epoch {i} | Loss: {losses[i]:.2f} | Acc: {accuracies[i]*100:.2f}%',\n fontsize=18, pad=40)\n```\n\ntext\n\n```\nfig = plt.figure(figsize=(12, 12))\nplt.axis('off')\nax = fig.add_subplot(projection='3d')\nplt.tick_params(left=False,\n bottom=False,\n labelleft=False,\n labelbottom=False)\n```\n\ntext\n\n```\nanim = animation.FuncAnimation(fig, animate, \n np.arange(0, 200, 10), interval=800, repeat=True)\nhtml = HTML(anim.to_html5_video())\ndisplay(html)\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/08/1CiegA_x7-oTYRgzl-WDpBw.gif)\nOur Graph Convolutional Network (GCN) has effectively learned embeddings that group similar nodes into **distinct clusters**. This enables the final linear layer to distinguish them into separate classes with ease.\nEmbeddings are not unique to GNNs: they can be found everywhere in deep learning. They don't have to be 3D either: actually, they rarely are. For instance, language models like [BERT](https://arxiv.org/abs/1810.04805) produce embeddings with 768 or even 1024 dimensions.\nAdditional dimensions store more information about nodes, text, images, etc. but they also create bigger models that are more difficult to train. This is why keeping low-dimensional embeddings as long as possible is advantageous.\n## Conclusion\nGraph Convolutional Networks are an incredibly versatile architecture that can be applied in **many contexts**. In this article, we familiarized ourselves with the PyTorch Geometric library and objects like `Datasets` and `Data`. Then, we successfully reconstructed a graph convolutional layer from the ground up. Next, we put theory into practice by implementing a GCN, which gave us an understanding of practical aspects and how individual components interact. Finally, we visualized the training process and obtained a clear perspective of what it involves for such a network.\nZachary's karate club is a simplistic dataset, but it is good enough to understand the most important concepts in graph data and GNNs. Although we only talked about node classification in this article, there are other tasks GNNs can accomplish: **link prediction** (e.g., to recommend a friend), **graph classification** (e.g., to label molecules), **graph generation** (e.g., to create new molecules), and so on.\nBeyond GCN, numerous GNN layers and architectures have been proposed by researchers. In the next article, we'll introduce the [Graph Attention Network](https://mlabonne.github.io/blog/gat/) (GAT) architecture, which dynamically computes the GCN's normalization factor and the importance of each connection with an attention mechanism.\nIf you want to know more about graph neural networks, dive deeper into the world of GNNs with my book, [Hands-On Graph Neural Networks](https://mlabonne.github.io/blog/book.html).\n## Next article\n> [**Chapter 2: Graph Attention Networks: Self-Attention Explained**](https://towardsdatascience.com/graph-attention-networks-in-python-975736ac5c0c)\n* * *\n_Learn more about machine learning and support my work with one click - become a Medium member here:_\n> [**Join Medium with my referral link - Maxime Labonne**](https://medium.com/@mlabonne/membership)\n_If you're already a member, you can_[ _follow me on Medium_](https://medium.com/@mlabonne) _._\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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