Graph Machine Learning Models
A Practical Comparison with Examples
In today’s data-driven world, relationships matter just as much as the data itself. Whether it’s social networks, molecular structures, or recommendation systems, the data we work with often isn’t just isolated points — there are intricate connections between them. This is where graph machine learning models come into play. Unlike traditional models that focus on individual data points, graph-based models capture the relationships between them, leveraging the powerful structure of graphs.
But what’s the best model for the job? From Graph Neural Networks (GNNs) to Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), each model has its strengths and challenges. In this article, we’ll explore the key graph ML models, compare them side by side, and walk through practical examples to help you decide which one suits your needs.
Graph Data and Why It’s Different
Before diving into the models, let’s start with the basics: What makes graph data special?
In a graph, you have two core components:
- Nodes (vertices) represent entities, like users in a social network or molecules in a chemical compound.
- Edges connect those nodes, representing…
