A Primer on Temporal Graph Learning
It provides an educational resource for learners in machine learning, but it is incremental as it synthesizes existing knowledge without new results.
This primer introduces temporal graph learning (TGL) by explaining key concepts and methods, including neural networks and time series forecasting, to enhance understanding of the framework.
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.