A New Fast Weighted All-pairs Shortest Path Search Algorithm Based on Pruning by Shortest Path Trees

TL;DR

This paper introduces a novel fast weighted all-pairs shortest path algorithm based on pruning with shortest path trees, significantly reducing access to adjacent vertices and outperforming traditional algorithms in various graph types.

Contribution

The paper presents a new weighted all-pairs shortest path algorithm utilizing shortest path trees to reduce vertex access, proving deadlock-freedom and demonstrating superior performance in many graph scenarios.

Findings

Reduces average vertex accesses ({lpha}) close to 1.

Outperforms Dijkstra in speed and access reduction in most graph types.

Outperforms Peng algorithm in dense and hypercube graphs, but not in sparse graphs.

Abstract

Recently we submitted a paper, whose title is A New Fast Unweighted All-pairs Shortest Path Search Algorithm Based on Pruning by Shortest Path Trees, to arXiv. This is related to unweighted graphs. This paper also presents a new fast all-pairs shortest path algorithm for weighted graph based on the same idea. In Dijkstra algorithm which is said to be fast in weighted graphs, the average number of accesses to adjacent vertices (expressed by {\alpha}) is about equal to the average degree of the graph. On the other hand, our algorithm utilizes the shortest path trees of adjacent vertices of each source vertex in the same manner as the algorithm for unweighted graphs, and reduce {\alpha} drastically in comparison with Dijkstra algorithm. Roughly speaking {\alpha} is reduced to the value close to 1, because the average degree of a tree is about 2, and one is used to come in and the other is used to go out, although that does not hold true when the depth of the short path trees is small. In case of weighted graphs, a problem which does not occur in unweighted graphs occurs. It is waiting for the generation of the shortest path tree of an adjacent vertex. Therefore, it is possible that a deadlock occurs. We prove our algorithm is deadlock-free. We compared our algorithm with Dijkstra and Peng algorithms. On Dijkstra algorithm ours outperforms it on speed and {\alpha} except that Dijkstra algorithm slightly outperforms ours or they are almost the same on CPU time in sparse scale-free graphs. The result on Peng algorithm is as follows: In speed and {\alpha}, ours outperforms Peng algorithm in hypercube-shaped and dense scale-free graphs, but conversely Peng algorithm outperforms ours in sparse scale-free graphs.