Adaptive Landmark Selection Strategies for Fast Shortest Path Computation in Large Real-World Graphs

This paper considers the task of answering shortest path queries in large real-world graphs such as social networks, communication networks and web graphs. The traditional Breadth First Search (BFS) approach for solving this problem is too time-consuming when networks with millions of nodes and possibly billions of edges are considered. A common technique to address these complexity issues uses a small set of landmark nodes from which the distance to all other nodes is precomputed in order to then answer arbitrary distance queries by navigating via one of the selected landmarks. Although many strategies to select landmarks have been introduced in previous work, the problem of finding an optimal set that covers the entire graph remains NP-hard. Our contribution starts with a study of characteristics that determine the successfulness of a landmark selection strategy. We propose a new adaptive heuristic for selecting landmarks that does not only pick central nodes, but also ensures that these landmarks properly cover different areas of the graph. Experiments on a diverse set of large graphs show that the proposed selection strategy and assisting node processing technique can efficiently estimate the node-to-node distance in graphs with millions of nodes with very high accuracy, while using the same amount of precomputation time as previously proposed strategies.