If you have spent any time learning about modern artificial intelligence, you have probably come across terms like neural networks, transformers, and large language models. But there is another type of neural network that becomes especially useful when the data is less like a spreadsheet and more like a network of connected things.

That is where a Graph Neural Network (GNN) comes in.

A graph neural network is designed to learn from relationships between data points. Instead of treating every piece of information as an isolated item, a GNN looks at how different entities are connected and uses those connections to make predictions.

This makes GNNs useful in areas ranging from social networks and recommendation systems to fraud detection, drug discovery, and knowledge graphs.

In this guide, we will look at what a graph neural network is, how it works, the major types of GNNs, where they are used, and some of the challenges researchers still face.

If you’re also exploring how modern AI models differ from one another, you may want to read our guide to ChatGPT vs Claude vs Gemini.

What Is a Graph Neural Network?

A Graph Neural Network is a type of deep learning model designed to work with graph-structured data.

A graph, in this context, is simply a collection of nodes and connections between them.

For example, imagine a social network. Each person can be represented as a node, while a friendship or following relationship can be represented as an edge connecting two nodes.

The same idea can be applied to many other situations.

In a shopping recommendation system:

• Users can be nodes.
• Products can be nodes.
• Purchases or interactions can be edges.

In a financial network:

• Bank accounts can be nodes.
• Transactions can be edges.
• In a molecule:
• Atoms can be nodes.
• Chemical bonds can be edges.

The important point is that the relationship between objects contains useful information. A traditional machine learning model may look primarily at the features of an individual object, while a GNN can also consider information from its neighboring objects.

That ability to learn from both features and relationships is what makes graph neural networks interesting.

What Is a Graph in Machine Learning?

Before understanding GNNs, it helps to understand what a graph actually represents.

A graph generally contains two fundamental elements: nodes and edges.

Nodes

Nodes represent entities or objects.

Depending on the application, a node could represent:

• A person
• A product
• A website
• A bank account
• A chemical atom
• A city
• A computer
• A research paper

Nodes can also have features. For example, a product node could contain information about its category, price, brand, and other characteristics.

Edges

Edges represent relationships between nodes.

For example:

• Two people are connected because they are friends.
• A user and product are connected because the user purchased it.
• Two websites are connected through a hyperlink.
• Two atoms are connected by a chemical bond.

Edges can sometimes have their own features as well. A financial transaction, for example, could contain information such as transaction amount or timestamp.

So, instead of looking at data as a simple list of rows and columns, a graph lets us represent how different pieces of information are related.

Students who are experimenting with AI tools can also explore our guide to generative AI tools for students for more practical examples.

How Does a Graph Neural Network Work?

The basic idea behind a GNN is easier to understand through its neighbors.

Imagine you want to understand one person in a social network. Looking only at that person’s profile may tell you something, but looking at the people connected to them can provide additional information.

A GNN uses this idea through a process commonly known as message passing or neighborhood aggregation.

A simplified process looks like this:

Graph → Collect information from neighbors → Aggregate information → Update node representation → Make prediction

Let’s break that down.

1. Represent the Data as a Graph

First, the real-world problem needs to be represented as a graph.

The model needs to know which entities are nodes and which relationships are edges.

For example, in a recommendation system, users and products could form different types of nodes, while interactions connect them.

2. Give Nodes Initial Features

Each node can have information associated with it.

A student node might contain information such as interests, courses, or other numerical features.

A product node could contain category and other product-related information.

These features provide the starting information for the model.

3. Collect Information From Neighboring Nodes

This is one of the most important parts of a GNN.

Instead of analyzing a node completely independently, the model looks at connected nodes and collects information from them.

This information is often called a message.

4. Aggregate the Information

The information received from neighboring nodes needs to be combined.

Different GNN architectures use different aggregation mechanisms.

The model may calculate something similar to a sum, mean, or a learned attention-based combination of neighboring information.

5. Update the Node Representation

The original node information and the information collected from its neighbors are then combined to create an updated representation.

After several layers, a node can contain information not only about itself but also about increasingly larger parts of the graph.

6. Make a Prediction

Finally, the learned representations can be used for different tasks.

If you want to experiment with GNNs yourself, PyTorch Geometric provides tools and documentation for building graph-based deep learning models with Python.

A GNN might predict:

• Which category a node belongs to
• Whether two nodes should be connected
• The properties of an entire graph
• A numerical value associated with a graph

What Are the Main Types of Graph Neural Networks?

There are several GNN architectures, each designed with different ideas and trade-offs.

Graph Convolutional Networks (GCNs)

Graph Convolutional Networks, or GCNs, are one of the most well-known approaches to graph learning.

A GCN allows nodes to combine their own information with information from neighboring nodes.

Despite the word “convolutional,” a GCN is not simply a CNN applied to a graph. It adapts the idea of learning from local neighborhoods to graph structures.

GCNs have been used for tasks such as node classification and graph-based prediction.

For a deeper visual explanation of graph convolutional networks, you can also explore Distill’s explanation of graph convolutional networks

Graph Attention Networks (GATs)

Graph Attention Networks introduce an attention mechanism into graph learning.

The basic idea is that not every neighboring node necessarily deserves the same amount of attention.

For example, imagine a user connected to many products. Some of those products may provide much more useful information about the user’s preferences than others.

A GAT can learn different levels of importance for neighboring information.

GraphSAGE

GraphSAGE, short for Graph Sample and Aggregate, was designed to make graph learning more practical for large graphs.

Rather than always processing every neighbor, GraphSAGE can sample a subset of neighbors and aggregate their information.

This approach is particularly useful when dealing with graphs that are too large to process in their entirety.

Graph Isomorphism Networks

Graph Isomorphism Networks, or GINs, focus on the ability of a GNN to distinguish different graph structures.

They are particularly relevant in research involving graph-level representation and graph classification.

Message Passing Neural Networks

Message Passing Neural Networks, often called MPNNs, provide a broader framework for thinking about how information moves through a graph.

They are particularly common in molecular machine learning, where atoms and bonds naturally form graph structures.

What Can Graph Neural Networks Be Used For?

GNNs can handle several different types of prediction problems.

Node Classification

In node classification, the goal is to determine the category of an individual node.

For example, a system might analyze a social network and identify whether an account belongs to a particular category based on its features and relationships.

Another example is classifying entities in a scientific network.

Link Prediction

Link prediction asks whether a relationship between two nodes is likely to exist.

This can be useful for:

• Friend recommendations
• Product recommendations
• Identifying potential connections
• Predicting relationships in biological networks

For example, if two users have many similar connections and interests, a recommendation system might predict that an interaction between them is possible.

Graph Classification

Here, the entire graph is classified.

One interesting example is molecular research.

A molecule can be represented as a graph where atoms are nodes and chemical bonds are edges. A GNN can then learn representations that may help predict properties of the molecule.

Graph Regression

Instead of predicting a category, a model can also predict a numerical value.

This can be useful when the target is a measurable property of a graph.

Real-World Applications of Graph Neural Networks

GNNs are not limited to academic research. Their ability to model relationships makes them relevant to several practical applications.

Recommendation Systems

Recommendation systems are a natural use case for graph-based models.

Users, products, videos, songs, and other items can be represented as nodes, while interactions create connections between them.

A GNN can use these relationships to learn patterns that may be useful for recommendations.

Fraud Detection

Financial transactions naturally form networks.

Imagine a large number of accounts connected through transactions. Instead of examining each transaction separately, a graph-based system can analyze patterns across connected accounts.

This can potentially help identify unusual structures or suspicious relationships.

Social Networks

Social platforms contain enormous graphs.

People connect with friends, follow accounts, share content, join communities, and interact with posts.

GNNs can potentially use these relationships for tasks such as node classification, recommendation, and other graph-based predictions.

Drug Discovery

Molecules can naturally be represented as graphs.

Atoms become nodes and chemical bonds become edges.

This makes GNNs particularly interesting in molecular machine learning, where researchers can use graph representations to model molecular structures and predict various properties.

Knowledge Graphs

A knowledge graph represents entities and relationships between them.

For example:

Person → works at → Company

or

Book → written by → Author

GNNs can be used to learn from these interconnected structures and support different graph-learning tasks.

Cybersecurity

Computer networks can also be represented as graphs.

Devices, accounts, servers, and other entities can act as nodes, while communication or other relationships become edges.

Graph-based approaches can therefore be useful for analyzing network relationships and detecting unusual patterns.

Graph Neural Network vs CNN

CNNs, or convolutional neural networks, are particularly well known for working with images.

An image has a regular grid structure. Each pixel has a predictable position relative to other pixels.

Graphs are different.

A graph can have an irregular structure. One node might have two neighbors while another might have hundreds.

A GNN is designed to work with this kind of irregular relational structure.

So, while CNNs are highly effective for grid-like data such as images, GNNs are designed specifically for data where relationships between entities are central to the problem.

Graph Neural Network vs Transformer

Transformers have become extremely important in modern AI, particularly for language and multimodal applications.

GNNs and transformers are not necessarily direct replacements for one another.

A GNN explicitly works with graph structure, while transformers commonly rely on attention mechanisms over sequences or sets of tokens.

There is also active research into combining graph-based methods with attention and transformer-style architectures.

The choice depends heavily on the structure of the data and the task being solved.

Advantages of Graph Neural Networks

One of the biggest advantages of GNNs is that they can learn from relationships instead of treating every data point as completely independent.

Other advantages include:

• They can model complex relationships.
• They can combine node features with graph structure.
• They can be applied to node, edge, and graph-level tasks.
• They are useful for naturally connected datasets.
• They can learn compact representations of graph data.

This makes them particularly valuable when the relationships themselves contain important information.

Limitations of Graph Neural Networks

GNNs are powerful, but they are not a universal solution.

Scalability

Very large graphs can require significant computational resources.

Social networks, financial networks, and web-scale graphs may contain millions or even billions of nodes and relationships.

Processing such graphs efficiently is an ongoing challenge.

Oversmoothing

When information is repeatedly passed between neighboring nodes across many layers, node representations can become increasingly similar.

This phenomenon is often referred to as oversmoothing.

It can make deeper GNNs difficult to train effectively.

Data Preparation

Building a useful graph from real-world data is not always straightforward.

The model needs meaningful nodes, relationships, and features. Poor graph construction can limit the quality of the results.

Interpretability

Like many deep learning models, GNNs can be difficult to interpret.

A model may produce a useful prediction, but understanding exactly why it reached that conclusion can require additional analysis.

A Simple Graph Neural Network Example

Let’s consider a movie recommendation system.

Suppose we have three users and several movies.

Each user is represented as a node, and each movie is also represented as a node.

Whenever a user watches or rates a movie, we create an edge between the user and that movie.

Now imagine that two users have watched many of the same movies.

A GNN can learn from these connections.

Instead of looking only at the movies a particular user watched, the model can also use information from related users and their interactions.

Over time, the learned representations can help the system identify patterns that may be useful for predicting which movies a user could be interested in.

This is a simplified example, but it demonstrates the central idea of graph learning: relationships provide information.

Why Are Graph Neural Networks Important for AI?

Much of the real world is connected.

People are connected to people. Customers interact with products. Websites link to other websites. Financial accounts exchange money. Atoms form molecules. Research papers cite other papers.

For students and researchers who want to explore AI beyond individual models, our guide to the best LLMs for research papers is another useful resource.

A traditional table can store information about these entities, but it does not always represent their relationships naturally.

Graphs provide a way to capture that structure.

GNNs then provide a machine learning framework for learning from it.

This is why graph neural networks remain an important area of research in artificial intelligence and machine learning.

The Future of Graph Neural Networks

Researchers continue to explore ways to make GNNs more scalable, efficient, interpretable, and capable of handling increasingly complicated data.

Some active areas include dynamic graphs, large-scale graph learning, graph transformers, molecular modeling, knowledge graphs, and combinations of graph learning with other AI architectures.

Another interesting direction is combining graph-based representations with language models and other foundation models.

Rather than thinking of GNNs as competing with every other AI architecture, it can be more useful to view them as one tool in a larger machine learning toolkit.

When the problem is fundamentally about relationships, graph-based learning can provide a representation that other models may not naturally capture.

Frequently Asked Questions

What is a graph neural network in simple terms?

A graph neural network is a deep learning model that learns from entities and the relationships connecting them. It represents entities as nodes and relationships as edges.

What is a GNN used for?

GNNs can be used for tasks such as node classification, link prediction, graph classification, recommendation systems, fraud detection, molecular property prediction, and knowledge-graph analysis.

Is a graph neural network deep learning?

Yes. GNNs are generally considered a family of deep learning models designed for graph-structured data.

What is message passing in GNN?

Message passing is the process through which a node collects information from its neighboring nodes and uses that information to update its representation.

What is the difference between GNN and CNN?

CNNs are commonly designed for grid-like data such as images, while GNNs are designed for irregular data represented through relationships between nodes.

Are graph neural networks difficult to learn?

The basic concept is relatively straightforward, but implementing advanced GNN architectures requires knowledge of machine learning, graph theory, and programming.

What are the most common GNN architectures?

Common approaches include Graph Convolutional Networks, Graph Attention Networks, GraphSAGE, Graph Isomorphism Networks, and Message Passing Neural Networks.

Final Thoughts

A Graph Neural Network provides a different way of thinking about machine learning.

Instead of asking only what information belongs to an individual data point, GNNs also ask an important question: how is that data point connected to everything around it?

That distinction matters in many real-world problems.

Whether the data represents users and products, financial transactions, molecules, websites, or scientific entities, relationships can contain valuable information.

GNNs provide a way to learn from those relationships and turn graph structures into useful representations for machine learning tasks. If you’re interested in practical AI applications as well, take a look at our collection of AI productivity tools

They still have challenges, especially around scalability, oversmoothing, data preparation, and interpretability. But as AI continues to move beyond simple tables and toward increasingly interconnected data, graph neural networks remain an important technology to understand.

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