Graph Transformers: The Next Generation of Graph Models
Graphs are ubiquitous, but leveraging their complex, long-range relationships has been a challenge for machine learning. Graph Neural Networks (GNNs) excel at capturing local patterns but struggle with global relationships. Enter Graph Transformers, which leverage powerful self-attention mechanisms, enabling each node to directly attend to information from anywhere in the graph, thus capturing richer relationships and subtle patterns. Compared to GNNs, Graph Transformers offer advantages in handling long-range dependencies, mitigating over-smoothing and over-squashing, and more effectively processing heterogeneous data. While Graph Transformers have higher computational complexity, techniques like sparse attention mechanisms and subgraph sampling enable efficient processing of large graph datasets.