This paper proposes a simple Transformer-based model, SimpleDyG, for dynamic graph modeling. Dynamic graphs are prevalent in web applications such as social networks, recommendation systems, and citation graphs. Existing methods often focus on structural dependencies and temporal changes but struggle with long-term dependencies and complex designs. SimpleDyG leverages the self-attention mechanism of Transformers to capture long-range dependencies in dynamic graphs. It re-conceptualizes dynamic graphs as a sequence modeling problem and introduces a novel temporal alignment technique. This technique captures temporal evolution patterns and streamlines the modeling process. The model is evaluated on four real-world datasets and shows competitive performance compared to state-of-the-art approaches despite its simple design. The key contributions include exploring the potential of Transformers for dynamic graphs, introducing a novel strategy to map dynamic graphs into sequences for scalability, and conducting extensive experiments across diverse domains. The results demonstrate the feasibility and superiority of SimpleDyG in dynamic graph modeling.This paper proposes a simple Transformer-based model, SimpleDyG, for dynamic graph modeling. Dynamic graphs are prevalent in web applications such as social networks, recommendation systems, and citation graphs. Existing methods often focus on structural dependencies and temporal changes but struggle with long-term dependencies and complex designs. SimpleDyG leverages the self-attention mechanism of Transformers to capture long-range dependencies in dynamic graphs. It re-conceptualizes dynamic graphs as a sequence modeling problem and introduces a novel temporal alignment technique. This technique captures temporal evolution patterns and streamlines the modeling process. The model is evaluated on four real-world datasets and shows competitive performance compared to state-of-the-art approaches despite its simple design. The key contributions include exploring the potential of Transformers for dynamic graphs, introducing a novel strategy to map dynamic graphs into sequences for scalability, and conducting extensive experiments across diverse domains. The results demonstrate the feasibility and superiority of SimpleDyG in dynamic graph modeling.