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Subgraph matching is a NP-complete problem that extracts isomorphicembeddings of a query graph $q$ in a data graph $G$. In this paper, we presenta framework with three components: Preprocessing, Reordering and Enumeration.While pruning is the core technique for almost all existing subgraph matchingsolvers, it mainly eliminates unnecessary enumeration over data graph withoutalternation of query graph. By formulating a problem: Assignment underConditional Candidate Set(ACCS), which is proven to be equivalent to Subgraphmatching problem, we propose Dynamic Graph Editing(DGE) that is for the firsttime designed to tailor the query graph to achieve pruning effect andperformance acceleration. As a result, we proposed DGEE(Dynamic Graph EditingEnumeration), a novel enumeration algorithm combines Dynamic Graph Editing andFailing Set optimization. Our second contribution is proposing fGQL , anoptimized version of GQL algorithm, that is utilized during the Preprocessingphase. Extensive experimental results show that the DGEE-based framework canoutperform state-of-the-art subgraph matching algorithms.

An Efficient Pruning Process with Locality Aware Exploration and Dynamic Graph Editing for Subgraph Matching