捕捉网络中高阶关系的短期影响与长期周期性，提升动态超边预测性能。
Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks
- 用双交互超边编码建模短期高阶动态
- 引入周期时间注入，发现长期关系重复规律
- 适合研究动态复杂网络演化的研究人员
现实世界网络中的对象存在高阶关系且随时间演化。通过深入分析，我们观察到高阶动态具有两个关键特征：(O1) 在短期内对其他关系产生结构与时间上的影响；(O2) 在长期内呈现周期性重现。本文提出LINCOLN方法，通过(1) 双交互超边编码捕捉短期模式，(2) 周期时间注入建模长期规律，(3) 中间节点表示增强表征能力。在动态超边预测任务上，LINCOLN优于九种前沿方法，验证了其有效性。
原文摘要 · Abstract (English)
Real-world networks have high-order relationships among objects and they evolve over time. To capture such dynamics, many works have been studied in a range of fields. Via an in-depth preliminary analysis, we observe two important characteristics of high-order dynamics in real-world networks: high-order relations tend to (O1) have a structural and temporal influence on other relations in a short term and (O2) periodically re-appear in a long term. In this paper, we propose LINCOLN, a method for Learning hIgh-order dyNamiCs Of reaL-world Networks, that employs (1) bi-interactional hyperedge encoding for short-term patterns, (2) periodic time injection and (3) intermediate node representation for long-term patterns. Via extensive experiments, we show that LINCOLN outperforms nine state-of-the-art methods in the dynamic hyperedge prediction task.
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