Introduction to Graph Transformers

Graphs are everywhere. From modeling molecular interactions and social networks to detecting financial fraud, learning from graph data is powerful—but inherently challenging. While Graph Neural Networks (GNNs) have opened up new possibilities by capturing local neighborhood patterns, they face limitations in handling complex, long-range relationships across the graph. Enter Graph Transformers, a new class of models designed to elegantly overcome these limitations through powerful self-attention mechanisms. Graph Transformers enable each node to directly attend to information from anywhere in the graph, capturing richer relationships and subtle patterns. In this article, we’ll introduce Graph Transformers, explore how they differ from and complement GNNs, and highlight why we believe this approach will soon become indispensable for data scientists and ML engineers alike. Where are Graph Transformers making an impact? Here are just a few examples of where they’re already proving powe ... Read full article.