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.

翻译：本文档旨在通过概念优先的方法帮助读者熟悉时间图学习（TGL）。我们系统性地介绍了理解TGL框架工作机制所需的关键概念。除了定性解释外，我们在适当之处融入了数学公式，以增强文本的清晰度。由于TGL涉及时间与空间学习，我们介绍了从循环神经网络、卷积神经网络到Transformer和图神经网络等相关学习架构。同时讨论了经典时间序列预测方法，以期为TGL提供可解释的学习解决方案。