Efficient Hypergraph Pattern Matching via Match-and-Filter and Intersection Constraint

TL;DR

This paper introduces a novel hypergraph pattern matching algorithm that leverages an intersection constraint, candidate hyperedge space, and a match-and-filter framework to significantly improve query processing efficiency.

Contribution

The paper proposes a new algorithm for hypergraph pattern matching that incorporates the intersection constraint and a candidate hyperedge space, enhancing speed and accuracy.

Findings

Outperforms state-of-the-art algorithms by up to orders of magnitude in query time

Uses intersection constraint to speed up verification process

Employs match-and-filter framework for efficient candidate maintenance

Abstract

A hypergraph is a generalization of a graph, in which a hyperedge can connect multiple vertices, modeling complex relationships involving multiple vertices simultaneously. Hypergraph pattern matching, which is to find all isomorphic embeddings of a query hypergraph in a data hypergraph, is one of the fundamental problems. In this paper, we present a novel algorithm for hypergraph pattern matching by introducing (1) the intersection constraint, a necessary and sufficient condition for valid embeddings, which significantly speeds up the verification process, (2) the candidate hyperedge space, a data structure that stores potential mappings between hyperedges in the query hypergraph and the data hypergraph, and (3) the Match-and-Filter framework, which interleaves matching and filtering operations to maintain only compatible candidates in the candidate hyperedge space during backtracking. Experimental results on real-world datasets demonstrate that our algorithm significantly outperforms the state-of-the-art algorithms, by up to orders of magnitude in terms of query processing time.