统一建模时序图中事件与时间的联合预测，提升复杂动态系统预判能力。
GTIN: A Unified Framework for Joint Event and Time Prediction in Temporal Graphs
- 构建统一数学框架，灵活捕捉时序图的多样结构与动态变化。
- 在多个数据集上优于现有方法，尤其在不规则事件模式下表现突出。
- 适合研究复杂动态系统、时序行为预测的学者与工程师参考。
时序图被广泛用于建模社交网络、金融网络和交通网络等动态系统。准确预测未来事件及其发生时间对理解与预见复杂行为至关重要，但该问题尚未得到充分研究。为此，本文提出一种统一的数学框架，可捕获时序图中不同复杂度的特性。该框架具有高度灵活性与表达力，能适应多种网络结构与时间动态。基于此，我们设计了一种新的联合预测方法，同时预测下一个事件及其发生时间。在多个数据集上的实证评估表明，该方法在不规则事件模式和复杂时间依赖场景下均显著优于现有技术，验证了本框架作为未来时序事件预测研究基础的潜力。
原文摘要 · Abstract (English)
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.
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