Fast shortest path distance estimation in large networks

In this paper we study approximate landmark-based methods for point-to-point distance estimation in very large networks. These methods involve selecting a subset of nodes as landmarks and computing offline the distances from each node in the graph to those landmarks. At runtime, when the distance between a pair of nodes is needed, it can be estimated quickly by combining the precomputed distances. We prove that selecting the optimal set of landmarks is an NP-hard problem, and thus heuristic solutions need to be employed. We therefore explore theoretical insights to devise a variety of simple methods that scale well in very large networks. The efficiency of the suggested techniques is tested experimentally using five real-world graphs having millions of edges. While theoretical bounds support the claim that random landmarks work well in practice, our extensive experimentation shows that smart landmark selection can yield dramatically more accurate results: for a given target accuracy,...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Michalis Potamias, Francesco Bonchi, Carlos Castillo, Aristides Gionis
• Keywords: Approximate Landmark-based Methods, CIKM 2009, Database, Large Networks, Random Landmarks