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

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Santos, BS (Author) Other; Silva, I (Author) Other; Costa, DG (Author) FEUP

Journal

Title: Symmetry

Vol. 15

Final page: 601

ISSN: 2073-8994

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00X-Z8S

DOI: 10.3390/sym15030601

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17