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Erschienen in:
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2023 | OriginalPaper | Buchkapitel

Graphlet-Based Measure to Assess Institutional Research Teams

verfasst von : Shengqing Li, Jiulei Jiang

Erschienen in: Big Data and Security

Verlag: Springer Nature Singapore

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Abstract

This paper identifies the microstructural characteristics of the research teams of academic institutions using graphlet-based measures. The results provide references for the evaluation and development of research teams. Scientific collaboration networks in the Top 20 institutions were evaluated using papers published in the past 6 years on computer image recognition in the field of artificial intelligence. The structural features of 3–5 node graphlets were extracted and analyzed. Significant graphlet structures were distinguished, and graphlet-based measures were used to determine the similarities and differences in the scientific collaboration networks. It was found that the graphlet structures contained significant information, and the graphlet correlation measures could be used to distinguish the similarities and differences of scientific research teams. The data can be used to investigate collaboration efficiency and develop and expand research teams.

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Metadaten
Titel
Graphlet-Based Measure to Assess Institutional Research Teams
verfasst von
Shengqing Li
Jiulei Jiang
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
Buch
Big Data and Security

Print ISBN: 978-981-9932-99-3

Electronic ISBN: 978-981-9933-00-6

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-981-99-3300-6_15

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