Modeling Tripartite Hyperevents in Scientific Collaboration Networks

2026-04-12

AI Summary

Existing approaches struggle to model the dynamic co-evolution among authors, references, and keywords in large-scale, fine-grained scientific collaboration data and cannot effectively test multiple competing hypotheses about the mechanisms of collective knowledge production. This work extends the Relational Hyper-Event Model (RHEM) to dynamic tripartite hypergraphs, introducing a unified framework that captures the joint generative process of multidimensional, heterogeneous entities within hyper-events of arbitrary size while explicitly controlling intra- and inter-set dependencies. The proposed method enables simultaneous modeling of complex interactions and rigorous statistical hypothesis testing on real-world scholarly data, offering an interpretable and quantifiable comparison of the drivers underlying collective knowledge creation.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• tripartite hyperevents
• scientific collaboration networks
• collective production
• relational hyperevent models
• multipartite social networks

Innovation

• Relational Hyperevent Models
• tripartite hypergraphs
• scientific collaboration networks
• collective production
• dynamic hypergraphs