How to Build a Graph Neural Network from Scratch

Are you ready to take your deep learning skills to the next level? Then you may want to consider learning how to build a Graph Neural Network (GNN) from scratch! For those who are unfamiliar, a GNN is a machine learning model designed to work with graph-structured data, such as social networks, chemical compounds, or even the connections between different parts of the brain.

So, how can you build a GNN from scratch? Well, it will certainly take some work, but with the right guidance, it is definitely achievable. In this article, we will guide you through the process step by step, so let’s get started!

Step 1: Understand the Basics

Before you start building your own GNN, it's important to understand the basics of graph theory and machine learning. At a minimum, we recommend having a good understanding of linear algebra, calculus, and probability theory. You should also be familiar with Python programming and have some experience working with libraries such as NumPy, PyTorch or TensorFlow.

Once you have a grasp on the basics, you can start learning more about GNNs themselves. At a high level, a GNN is a neural network that operates directly on graphs. These networks are made up of multiple layers, each of which applies a transformation to the graph’s vertices and edges. By stacking these layers on top of each other, a GNN can perform more and more complex computations on the graph.

Step 2: Choose Your Dataset

Now that you understand the basics, you are ready to select a dataset to work with. There are numerous publicly available graph datasets you can use to build and test your GNN, and you can always create your own graphs if you need a more specialized dataset.

When selecting a dataset, it is important to consider the type of graph you will be working with. For example, will it be a directed or undirected graph? What are the features of the graph’s nodes and edges? And what is the target task you are aiming to solve?

Some popular datasets for graph-based machine learning tasks include the Protein-Protein Interaction (PPI) dataset, the Cora citation dataset, and the Reddit comments dataset.

Step 3: Preprocess Your Data

Once you have a dataset, you need to preprocess it so that it can be used with your GNN. In general, this involves converting the raw data into a format that can be easily fed into a neural network.

For graph data, this may include converting the graph into a matrix or adjacency list representation, representing node and edge features as embeddings or arrays, and splitting the data into training and test sets. You may also need to perform additional preprocessing steps depending on the specifics of your dataset.

It is worth noting that preprocessing can be a time-consuming process, so it is important to stay organized and keep track of the steps you have taken.

Step 4: Construct Your GNN

Now that your data is preprocessed, you can start constructing your GNN. There are several different types of GNN architectures to choose from, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Recurrent Neural Networks (GRNNs). Each of these architectures has its own strengths and weaknesses, so it's essential to choose the one that best suits your needs.

Regardless of which architecture you choose, you will need to define several components, including the input layer, the hidden layers, and the output layer. Each layer should be designed to perform a specific task, such as processing information from the graph’s nodes or edges or reducing the dimensionality of the feature vectors.

Step 5: Train Your GNN

Once you have constructed your GNN, the next step is to train it using your preprocessed data. This will involve computing a loss function that measures how well the GNN is performing on the task you are trying to solve.

Training a GNN can be a computationally intensive process, so it is important to take steps to optimize your code and your hardware. For example, you may want to use a GPU to speed up the training process or use parallel processing to distribute the workload across multiple CPUs.

Step 6: Evaluate Your GNN

Finally, once your GNN is trained, you need to evaluate its performance. This may involve comparing its predictions to your test data, calculating metrics such as accuracy, precision, and recall, and visualizing the outputs to gain insights into how the model is working.

It is worth noting that evaluating a GNN can be a complex process, and the metrics you use may depend on the specific task you are trying to solve. In some cases, you may need to perform additional tuning or parameter optimization to improve the model’s performance.


Building a GNN from scratch requires a combination of deep learning and graph theory knowledge, as well as a strong understanding of Python programming and data preprocessing techniques. However, with the right guidance and plenty of practice, it is definitely achievable.

By following the steps outlined in this article, you can learn how to build your own GNN and apply it to a wide range of problems, from social network analysis to drug discovery. So why not get started today and join the growing community of graph-based deep learning researchers and practitioners?

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