Fast meta-analytic approximations for relational event models: applications to data streams and multilevel data.

IF 2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Journal of Computational Social Science Pub Date : 2024-01-01 Epub Date: 2024-06-08 DOI:10.1007/s42001-024-00290-7
Fabio Vieira, Roger Leenders, Joris Mulder
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引用次数: 0

Abstract

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'.

关系事件模型的快速元分析近似:数据流和多层次数据的应用。
由于最近的技术发展(如数字通信、在线数据库等),从大型网络中产生的大量关系-事件历史数据越来越容易获得。这为了解时态社交网络中参与者之间复杂的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,目前关系事件模型的主要瓶颈在于内存存储的限制和计算的复杂性。因此,关系事件模型主要用于相对较小的数据集,而包括多级数据结构和关系事件数据流在内的更大型、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的近似算法来解决这个问题,这种算法可以大大加快拟合关系型事件模型的速度,同时避免了计算问题。特别是,本文提出了用于分析关系事件数据流、多层次关系事件数据及其潜在组合的元分析近似值。通过数值模拟对这些方法的准确性和统计特性进行了评估。此外,还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 软件包 "remx "中实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Social Science
Journal of Computational Social Science SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
6.20
自引率
6.20%
发文量
30
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