While the landmark-based positional embedding approach offers several advantages, such as efficient representation of positional information and scalability, there are potential limitations and drawbacks that need to be addressed. One limitation is the sensitivity to landmark selection, as choosing landmarks based solely on degree centrality may not always capture the most relevant structural information in the graph. To mitigate this limitation, a more sophisticated selection criteria could be implemented, considering other node attributes or network properties. Another drawback is the reliance on a fixed set of landmarks, which may not adapt well to evolving graph structures. Introducing a mechanism for dynamic landmark selection or updating landmarks over time could help address this issue. Furthermore, the algorithm's performance may be impacted by the presence of noise or outliers in the graph, leading to suboptimal positional embeddings. Robust techniques for handling noisy data and outlier detection could enhance the algorithm's resilience to such challenges.