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. 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.