Graph Transformers enhance traditional graph neural networks by integrating attention mechanisms, allowing for more effective modeling of complex relationships within graph-structured data. They address limitations of message passing, enabling better scalability and richer representations. This innovation is pivotal for various applications across industries, including finance and life sciences.