Modeling Tripartite Hyperevents in Scientific Collaboration Networks

TL;DR

This paper introduces scalable Relational Hyperevent Models for analyzing large tripartite hypergraphs in scientific collaboration networks, enabling testing of complex interdependencies among actors, references, and keywords.

Contribution

It presents a novel application of RHEM to dynamic tripartite hypergraphs, addressing scalability and hypothesis testing in large scientific datasets.

Findings

Successfully modeled events linking actors, references, and keywords.

Enabled testing of inter-dependencies within and between sets.

Demonstrated applicability to real scientific network data.

Abstract

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.