Deep Graph Similarity Learning: A Survey
It addresses the need for effective similarity measures in graph-based domains, but is incremental as it is a survey rather than new research.
This paper surveys deep graph similarity learning, which tackles the problem of learning similarity metrics for graph-structured data to facilitate tasks like classification and clustering, by reviewing existing literature and proposing a taxonomy.
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.