Categorical Multi-Query Subgraph Matching on Labeled Graph

Subgraph matching stands as a fundamental issue within the research realm of graph analysis. In this paper, we investigate a novel combinatorial problem that encompasses both multigraph matching and subgraph matching. The objective of this investigation is to identify all data graphs within a larger graph that are isomorphic to the given query graphs. Firstly, multiple query graphs are collaborated through the design of a categorical graph, which aggregates similar query graphs into a single cluster. Following this, these similarity-clustered query graphs are integrated into a unified categorical graph. Secondly, a minimal isomorphic data graph is derived from a larger data graph, guided by the categorical graph. Additionally, an analysis of the inclusive and equivalence relationships among query nodes is conducted, with the aim of minimizing redundant matching computations. Simultaneously, all subgraph isomorphic mappings of the categorical graph onto the data graph are performed. Extensive empirical evaluations, conducted on both real and synthetic datasets, demonstrate that the proposed methods surpass the state-of-the-art algorithms in performance.