Vieira Fabio, Leenders Roger, Mulder Joris
Department Methodology and Statistics, Tilburg University, PO Box 90153, 5000 LE Tilburg, The Netherlands.
Department of Organization Studies, Tilburg University, Tilburg, The Netherlands.
J Comput Soc Sci. 2024;7(2):1823-1859. doi: 10.1007/s42001-024-00290-7. Epub 2024 Jun 8.
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 learn 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, multilevel relational event data and potentially 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 the publicly available R package 'remx'.
由于最近的技术发展（如数字通信、在线数据库等），源自大型网络的大型关系事件历史数据越来越容易获取。这为了解时间社交网络中参与者之间的复杂互动行为打开了许多新的大门。关系事件模型已成为关系事件历史分析的黄金标准。然而，目前拟合关系事件模型的主要瓶颈是计算性质的，表现为内存存储限制和计算复杂性。因此，关系事件模型主要用于相对较小的数据集，而更大、更有趣的数据集，包括多级数据结构和关系事件数据流，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这个问题，这些算法可以显著更快地拟合关系事件模型，同时避免计算问题。特别是，提出了元分析近似方法来分析关系事件数据流、多级关系事件数据及其潜在组合。使用数值模拟评估了这些方法的准确性和统计特性。此外，使用真实世界的数据来说明该方法在研究组织网络中的社会互动行为和政治参与者之间的互动行为方面的潜力。这些算法在公开可用的R包“remx”中实现。
