  This document aims to familiarize readers with temporal graph learning (TGL)
through a concept-first approach. We have systematically presented vital
concepts essential for understanding the workings of a TGL framework. In
addition to qualitative explanations, we have incorporated mathematical
formulations where applicable, enhancing the clarity of the text. Since TGL
involves temporal and spatial learning, we introduce relevant learning
architectures ranging from recurrent and convolutional neural networks to
transformers and graph neural networks. We also discuss classical time series
forecasting methods to inspire interpretable learning solutions for TGL.
