Fast Exact Shortest-Path Distance Queries on Large Networks by Pruned Landmark Labeling

TL;DR

This paper introduces a pruned landmark labeling technique that precomputes shortest-path distances efficiently on large networks, enabling fast exact queries even on graphs with hundreds of millions of edges.

Contribution

The paper presents a novel pruning strategy during BFS and a bitwise parallelization approach, significantly improving the scalability of exact shortest-path distance queries.

Findings

Handles large-scale social and web networks with hundreds of millions of edges.

Achieves query times comparable to previous methods on much larger graphs.

Demonstrates robustness and efficiency across various real-world networks.

Abstract

We propose a new exact method for shortest-path distance queries on large-scale networks. Our method precomputes distance labels for vertices by performing a breadth-first search from every vertex. Seemingly too obvious and too inefficient at first glance, the key ingredient introduced here is pruning during breadth-first searches. While we can still answer the correct distance for any pair of vertices from the labels, it surprisingly reduces the search space and sizes of labels. Moreover, we show that we can perform 32 or 64 breadth-first searches simultaneously exploiting bitwise operations. We experimentally demonstrate that the combination of these two techniques is efficient and robust on various kinds of large-scale real-world networks. In particular, our method can handle social networks and web graphs with hundreds of millions of edges, which are two orders of magnitude larger than the limits of previous exact methods, with comparable query time to those of previous methods.