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Hypergraphs are generalisation of graphs in which a hyperedge can connect anynumber of vertices. It can describe n-ary relationships and high-orderinformation among entities compared to conventional graphs. In this paper, westudy the fundamental problem of subgraph matching on hypergraphs (i.e,subhypergraph matching). Existing methods directly extend subgraph matchingalgorithms to the case of hypergraphs. However, this approach delays hyperedgeverification and underutilises the high-order information in hypergraphs, whichleads to large search space and high enumeration cost. Furthermore, with thegrowing size of hypergraphs, it is becoming hard to compute subhypergraphmatching sequentially. Thus, we propose an efficient and parallel subhypergraphmatching system, HGMatch, to handle subhypergraph matching in massivehypergraphs. We proposes a novel match-by-hyperedge framework to utilisehigh-order information in hypergraphs and uses set operations for efficientcandidates generation. Moreover, we develop an optimised parallel executionengine in HGMatch based on the dataflow model, which features a task-basedscheduler and fine-grained dynamic work stealing to achieve bounded memoryexecution and better load balancing. Experimental evaluation on 10 real-worlddatasets shows that HGMatch outperforms the extended version of thestate-of-the-art subgraph matching algorithms (CFL, DAF, CECI and RapidMatch)by orders of magnitude when using a single thread, and achieves almost linearscalability when the number of threads increases.

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