Shortest-Path Queries on Complex Networks: Experiments, Analyses, and Improvement

The shortest-path query, which returns the shortest path between two vertices, is a basic operation on complex networks and has numerous applications. To handle shortest-path queries, one option is to use traversal-based methods (e.g., breadth-first search); another option is to use extension-based methods, i.e., extending existing methods that use indexes to handle shortest-distance queries to support shortest-path queries. These two types of methods make different trade-offs in query time and space cost, but comprehensive studies of their performance on real-world graphs are lacking. Moreover, extension-based methods usually use extra attributes to extend the indexes, resulting in high space costs. To address these issues, we thoroughly compare the two types of methods mentioned above. We also propose a new extension-based approach, Monotonic Landmark Labeling (MLL), to reduce the required space cost while still guaranteeing query time. We compare the performance of different methods on ten large real-world graphs with up to 5.5 billion edges. The experimental results reveal the characteristics of various methods, allowing practitioners to select the appropriate method for a specific application.