Efficient Hypergraph Pattern Matching via Match-and-Filter and Intersection Constraint
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.
Cite
@article{arxiv.2512.10621,
title = {Efficient Hypergraph Pattern Matching via Match-and-Filter and Intersection Constraint},
author = {Siwoo Song and Wonseok Shin and Kunsoo Park and Giuseppe F. Italiano and Zhengyi Yang and Wenjie Zhang},
journal= {arXiv preprint arXiv:2512.10621},
year = {2025}
}