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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Acknowledgments
This work was supported by the Young Scholars Project of the Humanities and Social Sciences of the Ministry of Education in China (GrantNo.18YJC870011), the School Research Start-up Fund (Grant No. KYZ2018008Q), and the school's key funding for team projects (Grant No. TD22016). We thank the editor and reviewers for their comments that improved our paper.
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Li, S., Jiang, J. (2023). Graphlet-Based Measure to Assess Institutional Research Teams. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_15
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DOI: https://doi.org/10.1007/978-981-99-3300-6_15
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