This additional modeling power allows the Graph Transformer to better articulate how a vertex should be updated given the content of its neighbors, as well as to learn global patterns of graph structure relevant to the model's objective.
v is embedded in a dense continuous space by the embedding process described at the end of this section, resulting in matrix which will serve as input to the graph transformer model shown in Figure 4.