Frequent subgraph mining algorithms are widely used in various areas for information analysis. As yet, a handful of algorithms have been proposed and defined in the literature. While several experimental studies were reported, these experiments lack critical information which are important for selecting an implementation of an algorithm for a specific case of use. In this paper, we report on experiments that we carried out on available implementations of complete search Frequent Subgraph Mining (FSM) algorithms. These experiments are conducted in order to choose a suitable FSM solution (i.e., implementation). We identified 32 algorithms in the literature, six of them were selected for our experiments, through a filtering process relying on a set of criteria. Thirteen working implementations of these 6 algorithms are experimented. In this paper, we provide details of the experiments in terms of performance metrics and input variation effect. We propose a preliminary selection of the most efficient FSM solutions for end users, based on the most tested centralized graph-transaction datasets of the literature.

中文翻译：

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集中图事务数据库中完整搜索 FSM 实现的更新仪表板

频繁子图挖掘算法被广泛应用于信息分析的各个领域。迄今为止，文献中已经提出并定义了一些算法。虽然报告了几项实验研究，但这些实验缺乏关键信息，这些信息对于为特定使用情况选择算法的实现很重要。在本文中，我们报告了我们对完整搜索频繁子图挖掘 (FSM) 算法的可用实现进行的实验。进行这些实验是为了选择合适的 FSM 解决方案（即实现）。我们在文献中确定了 32 种算法，其中 6 种被选择用于我们的实验，通过依赖于一组标准的过滤过程。试验了这 6 种算法的 13 种工作实现。在本文中，我们在性能指标和输入变化效果方面提供了实验的详细信息。我们根据文献中经过最多测试的集中图交易数据集，为最终用户初步选择了最有效的 FSM 解决方案。