Landmark Selection Strategy to Accelerate Shortest Distance Queries

Computing the shortest path between vertices is a fundamental task in network analysis.This paper introduces an innovative strategy for selecting landmarks in a graph, which improves upon the traditional highest-degree selection method.Our approach captures the essential structural features of graphs, effectively reducing landmark overlap and clustering.Combined with partition querying and upper-bound pruning optimizations, our algorithm achieves a notable 20% -30% increase in query speed and is applicable to multi-million record datasets.This study offers new insights and solutions for enhancing query performance in large-scale graphstructured data, paving the way for further innovations in graph database optimization to meet the growing demands of large-scale graph data processing.