  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.
