Graph Neural Networks: Relationship Data
When your data has connections and relationships, GNNs are your specialized tool.
When Data Has Relationships
Most AI architectures assume data comes as sequences (text), grids (images), or independent rows (tabular). But much real-world data is fundamentally about relationships: social networks, molecular structures, transportation systems, recommendation engines.
A graph has nodes (entities) and edges (relationships). In a social network, people are nodes and friendships are edges. In a molecule, atoms are nodes and bonds are edges. GNNs are designed to learn from this structure.
How GNNs Work
GNNs learn by message passing—each node aggregates information from its neighbors, then updates its own representation. After several rounds, each node contains information about its extended neighborhood.
Round 1: Each person learns about their direct friends
Round 2: Each person learns about friends-of-friends
Round 3: Each person knows their extended social circle
After enough rounds, the network understands community structure, influence patterns, and connection strengths.
GNN Applications
| Application | Nodes | Edges | Prediction |
|---|---|---|---|
| Social networks | People | Friendships | New connections, communities |
| Recommendations | Users + Items | Interactions | What to recommend |
| Drug discovery | Atoms | Bonds | Molecular properties |
| Fraud detection | Accounts | Transactions | Suspicious patterns |
| Traffic prediction | Intersections | Roads | Congestion, routes |
| Knowledge graphs | Entities | Relations | Missing facts |
Pinterest: Uses GNNs for visual recommendations
Uber: Traffic prediction with road networks
Drug companies: Molecular property prediction
Financial institutions: Fraud ring detection
When to Consider GNNs
Ask yourself: Is the relationship between data points as important as the data points themselves?
If your problem involves networks, connections, or graph-structured data, GNNs are likely your best choice. If relationships don't matter, simpler architectures will work.
GNNs are more specialized than transformers or CNNs. You won't encounter them as frequently, but when your data is naturally graph-structured, they significantly outperform other architectures trying to work around the structure.