Higher-order interactions at scientific conferences influence team formation

TL;DR

This paper introduces a higher-order network framework to analyze how different types of interactions at scientific conferences influence the formation of collaborative teams, revealing the significance of various interaction patterns.

Contribution

It generalizes existing pairwise models to include group interactions over time, providing a formal taxonomy and applying it to real conference data.

Findings

All interaction types are highly significant for team formation

Higher-order interactions offer new insights into collective behavior

The framework can be applied to other social network contexts

Abstract

Cooperation enables teams to solve complex problems that one individual alone cannot address. In science, collaborative teams have become the predominant way through which progress is achieved. These scientific collaborations arise though various mechanisms, among which interactions at conferences. The Scialog conferences, which comprise a series of small, interdisciplinary scientific workshops held over several years, are an ideal laboratory to study the network mechanisms leading to team formation. Building on existing work studying team formation from a pairwise perspective, we present a higher-order network perspective generalizing this framework. We provide a formalization for the notion of group interaction over time by defining a taxonomy of synchronous and asynchronous group interactions. We apply this framework to the Scialog case study using a stepwise selection logistic model and find evidence that all interaction types described in our taxonomy are highly significant for team formation. This higher-order network perspective provides a new framework for the study of collective behavior and group formation.