What Are Graph Neural Networks? A Beginner's Guide

Most AI works with data organized in neat rows and columns—spreadsheets, essentially. Each row is independent: a customer record, an image, a text document. The AI processes each item separately.

But real-world data is often connected. Social networks connect people. Computer networks connect devices. Supply chains connect companies. Chemical molecules connect atoms. Trying to analyze connected data by ignoring connections is like trying to understand a conversation by only looking at individual words.

This is where Graph Neural Networks shine: they're designed specifically for data where relationships matter.

In computer science, a "graph" isn't a chart or diagram—it's a way of representing connected things.

Nodes: The things being connected. People in a social network. Devices in a computer network. Stations in a subway system.

Edges: The connections between things. Friendships, network cables, subway lines.

Any connected system can be represented as a graph. Facebook's social network is a graph with billions of nodes (users) and edges (friendships). The internet is a graph of devices and connections. Your brain is a graph of neurons and synapses.

A Graph Neural Network is an AI model that learns from graph-structured data. It understands not just individual nodes, but how they're connected and what those connections mean.

Here's the key insight: In a graph, nodes are defined by their neighbors. A well-connected person in a social network is different from an isolated one—not because of their individual attributes, but because of their connections.

GNNs learn by passing information between connected nodes. Each node collects information from its neighbors, processes it, and updates its own understanding. After several rounds of this "message passing," each node has learned about its local neighborhood, its neighbors' neighborhoods, and so on.

The result: GNNs understand context and relationships in ways that traditional AI cannot.

Imagine trying to identify fraud in a financial network. Traditional AI might look at each transaction independently: amount, time, merchant category.

A GNN sees the bigger picture: this account is connected to five other accounts that were all created the same day. Those accounts all received money from one source. They all made similar purchases. The transaction you're looking at might seem normal in isolation, but the pattern of connections screams fraud.

The GNN didn't need rules about fraud rings—it learned to recognize suspicious connection patterns from data.

GNNs learn through a process called "message passing":

Step 1 - Initialize: Each node starts with its own features. A computer in a network might have features like its operating system, open ports, and traffic volume.

Step 2 - Aggregate: Each node collects information from its neighbors. It might average their features, take the maximum, or combine them in learned ways.

Step 3 - Update: Each node updates its own representation by combining its current state with the aggregated neighbor information.

Step 4 - Repeat: Steps 2-3 happen multiple times. With each round, nodes learn about more distant neighbors.

Step 5 - Predict: The final node representations are used to make predictions—classifying nodes, predicting edges, or understanding the entire graph.

The magic happens because the aggregation and update functions are learned from data. The GNN figures out what information matters and how to combine it.

GNNs are transforming fields where relationships matter:

Drug Discovery: Molecules are graphs of atoms and bonds. GNNs predict drug properties and interactions, accelerating pharmaceutical research.

Recommendation Systems: User-product interactions form graphs. GNNs power recommendations at Pinterest, Uber, and other major platforms.

Traffic Prediction: Road networks are graphs. Google Maps uses GNNs to predict travel times by understanding how congestion spreads.

Social Network Analysis: Understanding influence, detecting fake accounts, and recommending connections all benefit from GNN analysis.

Cybersecurity: Computer networks are natural graphs. GNNs detect threats by understanding how attacks propagate through connected systems—our specialty at Hypergraph.

Cyber attacks aren't random events—they're connected sequences of actions. An attacker compromises one system, uses it to reach another, establishes persistence, moves laterally, and eventually achieves their objective.

Traditional security tools see individual events: a login here, a file access there, a network connection somewhere else. Each might look normal in isolation.

GNNs see the attack as a whole: these events are connected, forming a suspicious path through the network. The AI doesn't need rules describing every attack pattern—it learns to recognize malicious connectivity.

This is why GNNs are revolutionizing threat detection. They see what traditional tools miss.