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.

在将数据表示为图形的许多领域中，学习图形之间的相似性度量标准被认为是一个关键问题，它可以进一步促进各种学习任务，例如分类，聚类和相似性搜索。最近，人们对深度图相似性学习越来越感兴趣，其中的主要思想是学习一种深度学习模型，该模型将输入图映射到目标空间，以使目标空间中的距离近似于输入空间中的结构距离。在这里，我们对深度图相似性学习的现有文献进行全面回顾。我们为方法和应用提出了系统的分类法。最后，我们讨论了该问题的挑战和未来的方向。