提出可捕捉动态非线性关系的新型事件模型，揭示科研合作演化中的复杂规律。
Beyond Linearity and Time-Homogeneity: Relational Hyper Event Models with Time-Varying Non-Linear Effects
- 用张量积平滑建模统计量随时间变化的非线性影响
- 在真实科研合作数据中发现非单调演化模式，线性模型无法识别
- 适合研究复杂动态网络中的非线性因果关系，如学术合作演化
近年来技术进步使得大规模带时间戳的实体间关系事件网络收集变得容易。关系超事件模型（RHEMs）通过将事件发生率建模为基于历史统计和外部信息的函数，来解释这些事件的动态。然而，尽管数据复杂，当前大多数RHEM方法仍依赖线性假设。本文通过引入更灵活的模型，允许统计量的影响随时间非线性变化，突破这一限制。我们首次结合张量积平滑，同时建模时间与非线性的联合效应。在合成数据和真实数据上验证方法有效性，特别应用于分析科学合作模式及其影响力随时间的演变。结果揭示了线性模型无法捕捉的非单调动态特征，提供了对关系超事件驱动机制的深层理解。
原文摘要 · Abstract (English)
Recent technological advances have made it easier to collect large and complex networks of time-stamped relational events connecting two or more entities. Relational hyper-event models (RHEMs) aim to explain the dynamics of these events by modeling the event rate as a function of statistics based on past history and external information. However, despite the complexity of the data, most current RHEM approaches still rely on a linearity assumption to model this relationship. In this work, we address this limitation by introducing a more flexible model that allows the effects of statistics to vary non-linearly and over time. While time-varying and non-linear effects have been used in relational event modeling, we take this further by modeling joint time-varying and non-linear effects using tensor product smooths. We validate our methodology on both synthetic and empirical data. In particular, we use RHEMs to study how patterns of scientific collaboration and impact evolve over time. Our approach provides deeper insights into the dynamic factors driving relational hyper-events, allowing us to evaluate potential non-monotonic patterns that cannot be identified using linear models.
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