Because of its flexibility, intuitiveness, and expressivity, the graph edit distance (GED) is one of the most widely used distance measures for labeled graphs. Since exactly computing GED is NP-hard, over the past years, various heuristics have been proposed. They use techniques such as transformations to the linear sum assignment problem with error correction, local search, and linear programming to approximate GED via upper or lower bounds. In this paper, we provide a systematic overview of the most important heuristics. Moreover, we empirically evaluate all compared heuristics within an integrated implementation.

中文翻译：

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比较启发式图形编辑距离计算

由于其灵活性，直观性和表达性，图形编辑距离（GED）是标记图形使用最广泛的距离度量之一。由于精确计算GED是NP难的，因此在过去的几年中，已经提出了各种启发式方法。他们使用诸如对带有误差校正的线性和分配问题进行变换，局部搜索和线性编程等技术来通过上限或下限来近似GED。在本文中，我们提供了最重要的启发式方法的系统概述。此外，我们根据经验评估了集成实施中所有比较的启发式算法。