A subgraph is constructed by using a subset of vertices and edges of a given graph. There exist many graph properties that are hereditary for subgraphs. Hence, researchers from different communities have paid a great deal of attention in studying numerous subgraph problems, on top of the ordinary graph problems. Many algorithms are proposed in studying subgraph problems, where one common approach is by extracting the patterns and structures of a given graph. Due to the complex structures of certain types of graphs and to improve overall performances of the existing frameworks, machine learning techniques have recently been employed in dealing with various subgraph problems. In this article, we present a comprehensive review on five well known subgraph problems that have been tackled by using machine learning methods. They are subgraph isomorphism (both counting and matching), maximum common subgraph, community detection and community search problems. We provide an outline of each proposed method, and examine its designs and performances. We also explore non-learning-based algorithms for each problem and a brief discussion is given. We then suggest some promising research directions in this area, hoping that relevant subgraph problems can be tackled by using a similar strategy. Since there is a huge growth in employing machine learning techniques in recent years, we believe that this survey will serve as a good reference point to relevant research communities.

翻译：子图通过选取给定图的顶点和边的子集构建而成。存在许多对子图具有遗传性的图性质。因此，不同领域的研究者在常见图问题之外，对众多子图问题给予了高度关注。在研究子图问题时，提出了大量算法，其中一种常见方法是提取给定图的模式与结构。由于某些图类型的结构复杂性以及为提升现有框架的整体性能，近年来机器学习技术被用于处理各类子图问题。本文全面综述了利用机器学习方法解决的五大经典子图问题，分别为：子图同构（包含计数与匹配）、最大公共子图、社区检测与社区搜索问题。我们概述了每种方法的设计思路与性能表现，同时探讨了针对每个问题的非学习型算法并进行了简要讨论。在此基础上，提出该领域若干具有前景的研究方向，期望相关子图问题能通过类似策略得到解决。鉴于近年来机器学习技术的应用迅猛增长，本综述将为相关研究社群提供重要的参考支点。