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：源自大规模网络的海量关系事件历史数据，正因近期技术发展（如数字通信、在线数据库等）而日益普及。这为探索时序社交网络中行动者间的复杂互动行为开辟了新途径。关系事件模型已成为关系事件历史分析的黄金标准。然而，当前拟合关系事件模型的主要瓶颈在于计算层面——包括内存存储限制与计算复杂度。因此，关系事件模型主要适用于相对较小的数据集，而包含多层次数据结构与关系事件数据流在内的更大规模、更具价值的数据集，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的近似算法来解决该问题，这些算法能显著加快关系事件模型的拟合速度，同时避免计算难题。具体而言，本文提出了适用于关系事件数据流分析、多层次关系事件数据分析及其潜在组合的元分析近似方法。通过数值模拟评估了方法的精度与统计性质。此外，采用真实世界数据展示了该方法在组织网络社会互动行为研究与政治行动者互动行为研究中的潜力。相关算法已通过公开R包“remx”实现。