Large graphs are often used to simulate and model complex systems in various research and application fields. Because of its importance, frequent subgraph mining (FSM) in single large graphs is a vital issue, and recently, it has attracted numerous researchers, and played an important role in various tasks for both research and application purposes. FSM is aimed at finding all subgraphs whose number of appearances in a large graph is greater than or equal to a given frequency threshold. In most recent applications, the underlying graphs are very large, such as social networks, and therefore algorithms for FSM from a single large graph have been rapidly developed, but all of them have NP-hard (nondeterministic polynomial time) complexity with huge search spaces, and therefore still need a lot of time and memory to restore and process. In this article, we present an overview of problems of FSM, important phases in FSM, main groups of FSM, as well as surveying many modern applied algorithms. This includes many practical applications and is a fundamental premise for many studies in the future.

中文翻译：

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大图中的子图挖掘：综述

大图通常用于模拟和建模各种研究和应用领域的复杂系统。由于其重要性，单个大图中的频繁子图挖掘（FSM）是一个至关重要的问题，最近，它吸引了众多研究人员，并在研究和应用目的的各种任务中发挥了重要作用。FSM 旨在找到在大图中出现的次数大于或等于给定频率阈值的所有子图。在最近的应用中，底层图非常大，例如社交网络，因此从单个大图的 FSM 算法得到了快速发展，但它们都具有 NP-hard（非确定性多项式时间）复杂度和巨大的搜索空间，因此仍然需要大量的时间和内存来恢复和处理。在本文中，我们概述了 FSM 的问题、FSM 的重要阶段、FSM 的主要组，以及对许多现代应用算法的调查。这包括许多实际应用，是未来许多研究的基本前提。