Large relational-event history data stemming from large networks are becoming increasingly available due to recent technological developments (e.g. digital communication, online databases, etc). This opens many new doors to learning about complex interaction behavior between actors in temporal social networks. The relational event model has become the gold standard for relational event history analysis. Currently, however, the main bottleneck to fit relational events models is of computational nature in the form of memory storage limitations and computational complexity. Relational event models are therefore mainly used for relatively small data sets while larger, more interesting datasets, including multilevel data structures and relational event data streams, cannot be analyzed on standard desktop computers. This paper addresses this problem by developing approximation algorithms based on meta-analysis methods that can fit relational event models significantly faster while avoiding the computational issues. In particular, meta-analytic approximations are proposed for analyzing streams of relational event data and multilevel relational event data and potentially of combinations thereof. The accuracy and the statistical properties of the methods are assessed using numerical simulations. Furthermore, real-world data are used to illustrate the potential of the methodology to study social interaction behavior in an organizational network and interaction behavior among political actors. The algorithms are implemented in a publicly available R package 'remx'.

翻译：由大型网络生成的大规模关系事件历史数据因近期技术进步（如数字通信、在线数据库等）而日益可得，这为理解时态社交网络中行动者间的复杂互动行为开辟了众多新途径。关系事件模型已成为关系事件历史分析的金标准。然而，当前拟合关系事件模型的主要瓶颈在于计算层面，具体表现为内存存储限制与计算复杂性。因此，关系事件模型主要用于相对较小的数据集，而包含多层数据结构与关系事件数据流的更大、更有趣的数据集，则无法在标准台式计算机上进行分析。本文通过开发基于元分析方法、能够显著加速拟合过程并规避计算难题的近似算法来应对这一问题。具体而言，本文提出了基于元分析的近似方法，用于分析关系事件数据流、多层关系事件数据及其潜在组合。通过数值模拟评估了方法的精度与统计性质。此外，利用真实世界数据展示了该方法在研究组织网络中的社交互动行为及政治行动者间互动行为方面的潜力。相关算法已发布于公开可用的R包“remx”中。