A highway-centric labeling approach for answering distance queries on large sparse graphs

The distance query, which asks the length of the shortest path from a vertex $u$ to another vertex v, has applications ranging from link analysis, semantic web and other ontology processing, to social network operations. Here, we propose a novel labeling scheme, referred to as Highway-Centric Labeling, for answering distance queries in a large sparse graph. It empowers the distance labeling with a highway structure and leverages a novel bipartite set cover framework/algorithm. Highway-centric labeling provides better labeling size than the state-of-the-art $2$-hop labeling, theoretically and empirically. It also offers both exact distance and approximate distance with bounded accuracy. A detailed experimental evaluation on both synthetic and real datasets demonstrates that highway-centric labeling can outperform the state-of-the-art distance computation approaches in terms of both index size and query time.