  Graph Edit Distance (GED) is a popular similarity measurement for pairwise
graphs and it also refers to the recovery of the edit path from the source
graph to the target graph. Traditional A* algorithm suffers scalability issues
due to its exhaustive nature, whose search heuristics heavily rely on human
prior knowledge. This paper presents a hybrid approach by combing the
interpretability of traditional search-based techniques for producing the edit
path, as well as the efficiency and adaptivity of deep embedding models to
achieve a cost-effective GED solver. Inspired by dynamic programming,
node-level embedding is designated in a dynamic reuse fashion and suboptimal
branches are encouraged to be pruned. To this end, our method can be readily
integrated into A* procedure in a dynamic fashion, as well as significantly
reduce the computational burden with a learned heuristic. Experimental results
on different graph datasets show that our approach can remarkably ease the
search process of A* without sacrificing much accuracy. To our best knowledge,
this work is also the first deep learning-based GED method for recovering the
edit path.
