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.

中文翻译：

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通过动态嵌入对图编辑距离进行组合学习

图形编辑距离（GED）是成对图形的一种流行的相似性度量，它还指从源图形到目标图形的编辑路径的恢复。传统的A *算法由于其穷举性而遭受可伸缩性问题，其搜索启发式算法严重依赖于人类的先验知识。本文通过结合传统的基于搜索的技术来产生编辑路径，以及深度嵌入模型的效率和适应性，以实现具有成本效益的GED求解器，提出了一种混合方法。受动态编程的启发，以动态重用的方式指定节点级嵌入，并鼓励修剪次优分支。为此，我们的方法可以轻松地以动态方式集成到A *过程中，以及通过学习的启发式方法显着减少计算负担。在不同图形数据集上的实验结果表明，我们的方法可以显着简化A *的搜索过程，而不会牺牲很多准确性。据我们所知，这项工作也是第一个基于深度学习的GED方法，用于恢复编辑路径。