Sociological research has framed collective action in science, innovation, and culture as tripartite networks connecting teams of actors, lists of prior works, and sets of labels (e.g., keywords, topics). While methods for multipartite social networks were proposed decades ago, and have received a recent surge in interest, none of the suggested solutions scale to the size and granularity of contemporary data sets (scientific publications, patents, filmmaking) and at the same time allow for testing multiple competing hypotheses about the drivers of collective production. In this paper, we address this gap by applying Relational Hyperevent Models (RHEM) to dynamic tripartite hypergraphs. Using scientific networks as a case study, we model events linking any number of actors, references, and keywords, testing and controlling for inter-dependencies within and between each set.

翻译：社会学研究将科学、创新和文化中的集体行动视为连接行动者团队、先前作品列表和标签集（如关键词、主题）的三方网络。尽管多方社交网络的方法在几十年前就被提出，并且近年来受到广泛关注，但现有解决方案均无法兼顾当代数据集（科学出版物、专利、电影制作）的规模和粒度，同时支持对集体生产驱动因素的多重竞争性假设检验。本文通过将关系超事件模型（RHEM）应用于动态三方超图来填补这一空白。以科学网络为案例，我们构建了连接任意数量行动者、参考文献和关键词的事件模型，并对各集合内部及之间的相互依赖关系进行检验与控制。