GTIN：时态图中联合事件与时间预测的统一框架
GTIN: A Unified Framework for Joint Event and Time Prediction in Temporal Graphs
- Sharif University of Technology（谢里夫理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对时态图中联合预测下一个事件及其发生时间研究不足的问题，提出统一数学框架，在此基础上引入新方法，经多数据集实证评估，该方法优于现有技术，为时态事件预测研究奠定了坚实基础。
AI中文摘要：
时态图在社交网络、金融网络和交通网络等不同领域越来越多地用于建模动态系统。预测这些系统中的下一个事件及其发生时间对于理解和预测复杂行为至关重要，但此前研究较少。为填补这一空白，我们提出了一个统一的数学框架，能够捕捉不同程度的时态图复杂性。该框架灵活且富有表现力，可适应广泛的网络结构和时间动态。在此基础上，我们引入了联合预测下一个事件及其发生时间的新方法。多个数据集的实证评估表明，我们的方法始终优于现有技术，特别是在涉及不规则事件模式和复杂时间依赖性的场景中。这些发现凸显了我们框架作为时态事件预测未来研究坚实基础的潜力。
英文摘要：
Temporal graphs are increasingly used to model dynamic systems in diverse domains such as social networks, financial networks, and traffic networks. Predicting both what the next event will be and when it will occur in these systems is crucial for understanding and anticipating complex behaviors, but has not been studied much. To address this gap, we propose a unified mathematical framework capable of capturing varying degrees of complexity across temporal graphs. Our framework is flexible and expressive enough to accommodate a wide range of network structures and temporal dynamics. Building upon this analysis, we introduce our novel approach for jointly predicting the next event and its occurrence time. Empirical evaluations across multiple datasets demonstrate that our method consistently outperforms existing techniques, particularly in scenarios involving irregular event patterns and complex temporal dependencies. These findings highlight the potential of our framework as a robust foundation for future research in temporal event prediction.
