Efficient Landmark-Based Positional Embedding for Accurate Link Prediction in Graphs

The authors propose an efficient and effective representation of node positions in graphs using a small number of representative nodes called landmarks, which are selected based on degree centrality. They provide theoretical analysis on the achievable accuracy of distance estimates via landmarks for well-known random graph models, and leverage these insights to develop the Hierarchical Position embedding with Landmarks and Clustering (HPLC) algorithm for link prediction.