Fast exact shortest-path distance queries on large networks by pruned landmark labeling

By Takuya Akiba, Yoichi Iwata, Yuichi Yoshida. ACM SIGMOD Conference, 2013

426 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7a69631922f21615dbcd0bab250a515ffd08ac2d

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Route Planning in Transportation Networks (2015, methodology): “In general, any vertex ordering fully defines a labeling [5], and an ordering can be converted into the corresponding labeling efficiently [5,12].”