Graph matching algorithms are gaining more and more interest in the last years from different scientific communities; indeed, they allow comparing any kind of objects represented using their intrinsic structure, represented in terms of attributed relational graphs. The challenge is to make these algorithms able to provide solutions over huge graphs, with many thousands of nodes, and in a time that is adequate for practical applications; in this paper, we propose a comparison among the best performing algorithms available in the literature on a variety of very large graph databases used for performance assessment. The chosen datasets vary in terms of graph structure, size, density, presence of symmetric or repetitive substructures; this variability makes such datasets very challenging. The aim of this paper is to characterize the performance of the compared algorithms with respect to the typology, the size and other structural properties of the graphs; in this way, the user may consciously select the best suited algorithm for a given purpose. The results of an impressive experimentation that required 556 days of machine time are here presented and extensively discussed.

在过去的几年中，图匹配算法越来越受到不同科学界的关注。实际上，它们允许比较使用其固有结构表示的任何种类的对象，这些对象用属性关系图表示。挑战在于使这些算法能够在适合实际应用的时间内，通过具有数千个节点的庞大图形提供解决方案。在本文中，我们建议在用于性能评估的各种非常大的图形数据库上，对文献中可用的最佳性能算法进行比较。选择的数据集在图形结构，大小，密度，对称或重复子结构的存在方面有所不同。这种可变性使此类数据集非常具有挑战性。本文的目的是在图形的类型，大小和其他结构特性方面表征比较算法的性能。以这种方式，用户可以有意识地为给定目的选择最适合的算法。在此展示并广泛讨论了一项令人印象深刻的实验结果，需要556天的机器时间。