Fast Answering Pattern-Constrained Reachability Queries with Two-Dimensional Reachability Index

TL;DR

This paper introduces a two-dimensional reachability index that efficiently answers complex pattern-constrained reachability queries on large, labeled graphs, outperforming existing methods in size and speed.

Contribution

The paper proposes a novel two-dimensional reachability index for pattern-constrained queries, enabling efficient answers to NP-hard problems with improved performance over state-of-the-art techniques.

Findings

Index size and construction time are smaller than existing methods.

The index effectively prunes search space for complex queries.

Experimental results show significant performance improvements.

Abstract

Reachability queries ask whether there exists a path from the source vertex to the target vertex on a graph. Recently, several powerful reachability queries, such as Label-Constrained Reachability (LCR) queries and Regular Path Queries (RPQ), have been proposed for emerging complex edge-labeled digraphs. However, they cannot allow users to describe complex query requirements by composing query patterns. Here, we introduce composite patterns, a logical expression of patterns that can express complex constraints on the set of labels. Based on pattern, we propose pattern-constrained reachability queries (PCR queries). However, answering PCR queries is NP-hard. Thus, to improve the performance to answer PCR queries, we build a two-dimensional reachability (TDR for short) index which consists of a multi-way index (horizontal dimension) and a path index (vertical dimension). Because the number of combinations of both labels and vertices is exponential, it is very expensive to build full indices that contain all the reachability information. Thus, the reachable vertices of a vertex are decomposed into blocks, each of which is hashed into the horizontal dimension index and the vertical dimension index, respectively. The indices in the horizontal dimension and the vertical dimension serve as a global filter and a local filter, respectively, to prune the search space. Experimental results demonstrate that our index size and indexing time outperform the state-of-the-art label-constrained reachability indexing technique on 16 real datasets. TDR can efficiently answer pattern-constrained reachability queries, including label-constrained reachability queries.