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Related Experiment Video

Updated: Nov 14, 2025

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Multilayer representation of collaboration networks with higher-order interactions.

E Vasilyeva1,2, A Kozlov1, K Alfaro-Bittner3,4

  • 1Moscow Institute of Physics and Technology, 9 Institutskiy Per., Dolgoprudny, 141701, Moscow, Russia.

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|March 12, 2021
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Summary

This study introduces higher-order scientific collaboration networks to better represent team research beyond pairwise interactions. Analyzing these networks reveals insights into researcher roles and network evolution across various scientific fields.

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Area of Science:

  • Bibliometrics
  • Network Science
  • Scientific Collaboration Analysis

Background:

  • Traditional collaboration networks primarily model pairwise interactions, limiting their ability to represent complex, multi-person research teams.
  • Understanding collaboration dynamics is crucial for scientific innovation and knowledge discovery in research groups of all sizes.

Purpose of the Study:

  • To develop and analyze higher-order scientific collaboration networks that capture multi-person interactions.
  • To gain microscopic insights into researcher representativeness and network evolution within scientific collaborations.
  • To validate findings using both researcher and publication nodes across diverse scientific disciplines.

Main Methods:

  • Construction of higher-order collaboration networks representing interactions among three or more individuals.
  • Analysis of layered networks based on the number of collaborators.
  • Sequential graph analysis tracking the maturation of network topological features.
  • Node representation comparison using researchers and publications.

Main Results:

  • Higher-order networks provide a more natural representation of team-based scientific collaborations.
  • Analysis reveals novel insights into individual researcher roles and network structures.
  • The maturation process of collaboration networks can be tracked by progressively merging smaller collaborations.
  • Robustness of findings confirmed by using both researcher and publication nodes.

Conclusions:

  • Higher-order collaboration networks offer a powerful framework for understanding complex scientific teamwork.
  • This approach enhances the analysis of researcher representativeness and network dynamics.
  • The findings are applicable across physics, mathematics, computer science, and broader research fields.