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 m...