  In many domains where data are represented as graphs, learning a similarity
metric among graphs is considered a key problem, which can further facilitate
various learning tasks, such as classification, clustering, and similarity
search. Recently, there has been an increasing interest in deep graph
similarity learning, where the key idea is to learn a deep learning model that
maps input graphs to a target space such that the distance in the target space
approximates the structural distance in the input space. Here, we provide a
comprehensive review of the existing literature of deep graph similarity
learning. We propose a systematic taxonomy for the methods and applications.
Finally, we discuss the challenges and future directions for this problem.
