Temporal interaction graphs (TIGs), defined by sequences of timestamped interaction events, have become ubiquitous in real-world applications due to their capability to model complex dynamic system behaviors. As a result, temporal interaction graph representation learning (TIGRL) has garnered significant attention in recent years. TIGRL aims to embed nodes in TIGs into low-dimensional representations that effectively preserve both structural and temporal information, thereby enhancing the performance of downstream tasks such as classification, prediction, and clustering within constantly evolving data environments. In this paper, we begin by introducing the foundational concepts of TIGs and emphasize the critical role of temporal dependencies. We then propose a comprehensive taxonomy of state-of-the-art TIGRL methods, systematically categorizing them based on the types of information utilized during the learning process to address the unique challenges inherent to TIGs. To facilitate further research and practical applications, we curate the source of datasets and benchmarks, providing valuable resources for empirical investigations. Finally, we examine key open challenges and explore promising research directions in TIGRL, laying the groundwork for future advancements that have the potential to shape the evolution of this field.

本研究针对时序交互图（TIGs）在建模复杂动态系统中的应用提出了一个系统性的分析，填补了现有研究在时序依赖性处理上的空白。论文提出了一种全面的TIGRL方法分类体系，并借助于详细的数据集和基准资源，促进了后续研究及应用的开展。研究结果强调了时序交互图表示学习的关键挑战及未来研究方向，推动该领域的发展。 

时序交互图表示学习的调查：进展、挑战与机遇