This paper presents graphkit-learn, the first Python library for efficient computation of graph kernels based on linear patterns, able to address various types of graphs. Graph kernels based on linear patterns are thoroughly implemented, each with specific computing methods, as well as two well-known graph kernels based on non-linear patterns for comparative analysis. Since computational complexity is an Achilles’ heel of graph kernels, we provide several strategies to address this critical issue, including parallelization, the trie data structure, and the FCSP method that we extend to other kernels and edge comparison. All proposed strategies save orders of magnitudes of computing time and memory usage. Moreover, all the graph kernels can be simply computed with a single Python statement, thus are appealing to researchers and practitioners. For the convenience of use, an advanced model selection procedure is provided for both regression and classification problems. Experiments on synthesized datasets and 11 real-world benchmark datasets show the relevance of the proposed library.

本文介绍了graphkit-learn，这是第一个用于基于线性模式高效计算图内核的Python库，能够处理各种类型的图。彻底实现了基于线性模式的图形内核，每种都有特定的计算方法，另外还实现了两个基于非线性模式的知名图形内核进行比较分析。由于计算复杂性是图内核的致命弱点，因此我们提供了几种解决此关键问题的策略，包括并行化，特里数据结构以及扩展到其他内核和边缘比较的FCSP方法。所有提出的策略都可以节省大量的计算时间和内存使用量。而且，所有图形内核都可以用一个Python语句简单地计算出来，因此吸引了研究人员和从业人员。为了方便使用，提供了针对回归和分类问题的高级模型选择程序。在合成数据集和11个现实基准数据集上进行的实验表明了该库的相关性。