When applying the sequential training approach to large-scale, real-world graph datasets, several challenges and trade-offs may arise. One challenge is the increased computational complexity and memory requirements as the size of the graph grows, leading to longer training times and higher resource utilization. Additionally, ensuring the consistency and accuracy of the embeddings across different training iterations can be challenging, especially when dealing with evolving graph structures. Trade-offs may include sacrificing some level of accuracy for faster training times or optimizing the model for specific performance metrics at the expense of generalizability. Balancing these trade-offs while maintaining the efficiency and effectiveness of the sequential training approach is crucial for its successful application to large-scale graph datasets.