Efficiently Answering Quality Constrained Shortest Distance Queries in Large Graphs

TL;DR

This paper introduces a novel index structure and query algorithm for efficiently computing shortest distances in large graphs with quality constraints on edges, addressing a critical need in graph analytics.

Contribution

It proposes a new 2-hop labeling based index with path dominance relationships to handle quality-constrained shortest distance queries efficiently.

Findings

The index structure is minimal and effective.

Query processing is significantly faster than baseline methods.

Experimental results confirm the approach's efficiency on real datasets.

Abstract

The shortest-path distance is a fundamental concept in graph analytics and has been extensively studied in the literature. In many real-world applications, quality constraints are naturally associated with edges in the graphs and finding the shortest distance between two vertices $s$ and $t$ along only valid edges (i.e., edges that satisfy a given quality constraint) is also critical. In this paper, we investigate this novel and important problem of quality constraint shortest distance queries. We propose an efficient index structure based on 2-hop labeling approaches. Supported by a path dominance relationship incorporating both quality and length information, we demonstrate the minimal property of the new index. An efficient query processing algorithm is also developed. Extensive experimental studies over real-life datasets demonstrates efficiency and effectiveness of our techniques.