Efficient Single-Source Shortest Path and Distance Queries on Large Graphs

TL;DR

This paper introduces Highways-on-Disk (HoD), a novel disk-based index for efficiently answering single-source distance and shortest path queries on large, directed, and weighted graphs, outperforming existing methods.

Contribution

The paper presents HoD, a new disk-resident indexing technique that supports SSD and SSSP queries on large directed and weighted graphs, addressing limitations of prior I/O-efficient solutions.

Findings

HoD significantly improves query efficiency on large real-world graphs.

HoD supports directed and weighted graphs, unlike previous methods.

Experimental results show superior performance over existing solutions.

Abstract

This paper investigates two types of graph queries: {\em single source distance (SSD)} queries and {\em single source shortest path (SSSP)} queries. Given a node $v$ in a graph $G$, an SSD query from $v$ asks for the distance from $v$ to any other node in $G$, while an SSSP query retrieves the shortest path from $v$ to any other node. These two types of queries are fundamental building blocks of numerous graph algorithms, and they find important applications in graph analysis, especially in the computation of graph measures. Most of the existing solutions for SSD and SSSP queries, however, require that the input graph fits in the main memory, which renders them inapplicable for the massive disk-resident graphs commonly used in web and social applications. The only exceptions are a few techniques that are designed to be I/O efficient, but they all focus on undirected and/or unweighted graphs, and they only offer sub-optimal query efficiency. To address the deficiency of existing work, this paper presents {\em Highways-on-Disk (HoD)}, a disk-based index that supports both SSD and SSSP queries on directed and weighted graphs. The key idea of HoD is to augment the input graph with a set of auxiliary edges, and exploit them during query processing to reduce I/O and computation costs. We experimentally evaluate HoD on both directed and undirected real-world graphs with up to billions of nodes and edges, and we demonstrate that HoD significantly outperforms alternative solutions in terms of query efficiency.