Symmetry in Scientific Collaboration Networks: A Study Using Temporal Graph Data Science and Scientometrics

This article proposes a novel approach that leverages graph theory, machine learning, and graph embedding to evaluate research groups comprehensively. Assessing the performanceand impact of research groups is crucial for funding agencies and research institutions, but many traditional methods often fail to capture the complex relationships between the evaluated elements.In this sense, our methodology transforms publication data into graph structures, allowing the visualization and quantification of relationships between researchers, publications, and institutions.By incorporating symmetry properties, we offer a more in-depth evaluation of research groups cohesiveness and structure over time. This temporal evaluation methodology bridges the gap between unstructured scientometrics networks and the evaluation process, making it a valuable tool for decision-making procedures. A case study is defined to demonstrate the potential to providevaluable insights into the dynamics and limitations of research groups, which ultimately reinforces the feasibility of the proposed approach when supporting decision making for funding agencies andresearch institutions.

Automated summary

This article proposes a novel approach that integrates graph theory, machine learning, and graph embedding to comprehensively evaluate research groups. Traditional methods often fail to capture the complex relationships between evaluated elements, but our methodology transforms publication data into graph structures, visualizing and quantifying relationships between researchers, publications, and institutions. By incorporating symmetry properties, we offer a deeper evaluation of research group cohesiveness and structure over time. This temporal evaluation methodology bridges the gap between unstructured scientometric networks and the evaluation process, making it a valuable tool for decision-making procedures. A case study demonstrates the potential of the proposed approach to provide valuable insights into the dynamics and limitations of research groups, reinforcing its feasibility for supporting decision making in funding agencies and research institutions.