Efficient Subhypergraph Containment Queries on Hypergraph Databases

Abstract

In the real world, many complex systems consist of a large number of interacting groups of entities. A hypergraph consists of vertices and hyperedges that can connect multiple vertices. Since hypergraphs can effectively simulate complex intergroup relationships among entities, they have a wide range of applications such as computer vision and bioinformatics. In this paper, we study the subhypergraph containment query problem which is one of the most basic problems in the processing of hypergraphs. Existing methods on the subgraph query are designed for ordinary graphs and do not consider hypergraph features. If they are directly applied to subhypergraph containment query, they will suffer from hyperedge semantic incompleteness and label diversity sensitivity issues, resulting in inefficient algorithm performance. This motivates us to improve the performance by exploiting hyperedge features. In our work, we propose a novel framework for subhypergraph containment query called hyperedge filtering vertex testing. Based on the features of hypergraph, we propose an efficient filtering algorithm that can reduce the cost of the traditional filtering stage. In addition, we further propose efficient isomorphism testing techniques based on hyperedge vertex candidates to improve the performance. Extensive experiments on real datasets validate the superiority of our algorithm compared to existing methods.