  Subgraph matching is a NP-complete problem that extracts isomorphic
embeddings of a query graph $q$ in a data graph $G$. In this paper, we present
a framework with three components: Preprocessing, Reordering and Enumeration.
While pruning is the core technique for almost all existing subgraph matching
solvers, it mainly eliminates unnecessary enumeration over data graph without
alternation of query graph. By formulating a problem: Assignment under
Conditional Candidate Set(ACCS), which is proven to be equivalent to Subgraph
matching problem, we propose Dynamic Graph Editing(DGE) that is for the first
time designed to tailor the query graph to achieve pruning effect and
performance acceleration. As a result, we proposed DGEE(Dynamic Graph Editing
Enumeration), a novel enumeration algorithm combines Dynamic Graph Editing and
Failing Set optimization. Our second contribution is proposing fGQL , an
optimized version of GQL algorithm, that is utilized during the Preprocessing
phase. Extensive experimental results show that the DGEE-based framework can
outperform state-of-the-art subgraph matching algorithms.
