The insights from the theoretical analysis conducted for link prediction using hierarchical position embedding with landmarks and clustering can be extended to other graph-related tasks beyond link prediction, such as node classification or graph clustering. For node classification, the concept of hierarchical position embedding with landmarks can be leveraged to encode structural information about nodes in a graph, enabling more effective node representation learning. By incorporating landmark-based positional information into node classification models, nodes can be better differentiated based on their relative positions in the graph, leading to improved classification accuracy. Similarly, for graph clustering tasks, the hierarchical grouping of clusters based on landmarks can facilitate the identification of densely connected subgraphs within a larger graph. This hierarchical clustering approach, guided by landmark nodes, can enhance the efficiency and effectiveness of graph clustering algorithms by capturing the underlying structural hierarchy of the graph. Overall, the theoretical insights from the positional embedding approach can be applied to various graph analysis tasks, offering a principled and effective way to incorporate structural information for improved performance.