  Explainability of recommender systems has become essential to ensure users'
trust and satisfaction. Various types of explainable recommender systems have
been proposed including explainable graph-based recommender systems. This
review paper discusses state-of-the-art approaches of these systems and
categorizes them based on three aspects: learning methods, explaining methods,
and explanation types. It also explores the commonly used datasets,
explainability evaluation methods, and future directions of this research area.
Compared with the existing review papers, this paper focuses on explainability
based on graphs and covers the topics required for developing novel explainable
graph-based recommender systems.
