PSPC: Efficient Parallel Shortest Path Counting on Large-Scale Graphs

TL;DR

This paper introduces a parallel shortest path counting method for large-scale graphs that overcomes dependency barriers in index construction, achieving near-linear speedup with multiple threads and verified by empirical tests.

Contribution

It presents a novel parallel algorithm for shortest path counting that reduces dependencies, enabling efficient large-scale graph analysis.

Findings

Achieves approximately linear index time speedup with increasing threads.

Demonstrates efficiency and effectiveness through empirical evaluations.

Addresses dependency issues in parallel shortest path counting methods.

Abstract

In modern graph analytics, the shortest path is a fundamental concept. Numerous \rrev{recent works} concentrate mostly on the distance of these shortest paths. Nevertheless, in the era of betweenness analysis, the counting of the shortest path between $s$ and $t$ is equally crucial. \rrev{It} is \rev{also} an important issue in the area of graph databases. In recent years, several studies have been conducted in an effort to tackle such issues. Nonetheless, the present technique faces a considerable barrier to parallel due to the dependencies in the index construction stage, hence limiting its application possibilities and wasting the potential hardware performance. To address this problem, we provide a parallel shortest path counting method that could avoid these dependencies and obtain approximately linear index time speedup as the number of threads increases. Our empirical evaluations verify the efficiency and effectiveness.