Hypergraph patterns and collaboration structure

TL;DR

This paper introduces m-patterns to quantify collaboration relationships in hypergraphs, analyzing their prevalence in empirical data versus a null model, revealing significant differences in collaboration structures and success rates across contexts.

Contribution

It proposes a new formalization of collaboration relationships through m-patterns and compares their occurrence in real data to a null model, uncovering structural and success-related insights.

Findings

Certain collaboration structures are over- or underrepresented in empirical data.

COVID-19 scientific collaboration structures differ significantly from non-COVID-19.

Repeat collaborations show varying success depending on the number of authors.

Abstract

Humans collaborate in different contexts such as in creative or scientific projects, in workplaces and in sports. Depending on the project and external circumstances, a newly formed collaboration may include people that have collaborated before in the past, and people with no collaboration history. Such existing relationships between team members have been reported to influence the performance of teams. However, it is not clear how existing relationships between team members should be quantified, and whether some relationships are more likely to occur in new collaborations than others. Here we introduce a new family of structural patterns, m-patterns, which formalize relationships between collaborators and we study the prevalence of such structures in data and a simple random-hypergraph null model. We analyze the frequency with which different collaboration structures appear in our null model and show how such frequencies depend on size and hyperedge density in the hypergraphs. Comparing the null model to data of human and non-human collaborations, we find that some collaboration structures are vastly under- and overrepresented in empirical datasets. Finally, we find that structures of scientific collaborations on COVID-19 papers in some cases are statistically significantly different from those of non-COVID-19 papers. Examining citation counts for 4 different scientific fields, we also find indications that repeat collaborations are more successful for 2-author scientific publications and less successful for 3-author scientific publications as compared to other collaboration structures.