PeeK: A Prune-Centric Approach for K Shortest Path Computation

The K shortest path (KSP) algorithm, which finds the top K shortest simple paths from a source to a target vertex, has a wide range of real-world applications, e.g., routing, vulnerability detection, and biology analysis. While the top K shortest simple paths offer invaluable insights, computing them is time-consuming. For example, on a Twitter graph (61.6M vertices and 1.5B edges), the best parallel method needs about 20 minutes to get 128 shortest paths between two vertices. A key observation we made is existing works search K shortest paths from the original graph, while top K shortest paths only cover a meager portion of the original graph, e.g., less than 0.001% on a Twitter graph for K = 128.