HGMatch: A Match-by-Hyperedge Approach for Subgraph Matching on Hypergraphs

TL;DR

HGMatch introduces a novel parallel hyperedge-based framework for efficient subhypergraph matching in large hypergraphs, significantly outperforming existing methods in speed and scalability.

Contribution

The paper presents a new match-by-hyperedge approach and a parallel execution engine for subhypergraph matching, leveraging high-order information and advanced scheduling.

Findings

Outperforms state-of-the-art algorithms by orders of magnitude in single-threaded scenarios.

Achieves near-linear scalability with increasing threads.

Effectively handles massive hypergraphs with improved efficiency.

Abstract

Hypergraphs are generalisation of graphs in which a hyperedge can connect any number of vertices. It can describe n-ary relationships and high-order information among entities compared to conventional graphs. In this paper, we study the fundamental problem of subgraph matching on hypergraphs (i.e, subhypergraph matching). Existing methods directly extend subgraph matching algorithms to the case of hypergraphs. However, this approach delays hyperedge verification and underutilises the high-order information in hypergraphs, which leads to large search space and high enumeration cost. Furthermore, with the growing size of hypergraphs, it is becoming hard to compute subhypergraph matching sequentially. Thus, we propose an efficient and parallel subhypergraph matching system, HGMatch, to handle subhypergraph matching in massive hypergraphs. We proposes a novel match-by-hyperedge framework to utilise high-order information in hypergraphs and uses set operations for efficient candidates generation. Moreover, we develop an optimised parallel execution engine in HGMatch based on the dataflow model, which features a task-based scheduler and fine-grained dynamic work stealing to achieve bounded memory execution and better load balancing. Experimental evaluation on 10 real-world datasets shows that HGMatch outperforms the extended version of the state-of-the-art subgraph matching algorithms (CFL, DAF, CECI and RapidMatch) by orders of magnitude when using a single thread, and achieves almost linear scalability when the number of threads increases.