Graphormer extends the success of transformers to graph applications, addressing the unique challenges of complex relational data. By building upon the success of transformers in sequential data processing, Graphormer presents an exciting advancement in deep learning architectures tailored for graph-structured data, addressing the unique challenges posed by complex relational data. Many examples of graphstructured data, such as social networks, molecular structures, or recommendation systems (e.g., YouTube, Spotify), necessitate the modeling of intricate structural relationships between entities. Graphormer accomplishes this by integrating multi-headed self-attention mechanisms, where several attention heads allow the model to selectively attend to each node and its relationship to its neighbors.