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Graphlet-Based Measure to Assess Institutional Research Teams

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Big Data and Security (ICBDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1796))

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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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References

  1. Zhu, J.: Network epistemology in scientific collaboration: how do scientists’ social interactions influence cognitive interactions. Social Sciences 5, 122–130 (2021)

    Google Scholar 

  2. Zhang, X., Liu, H., Zhang, Z.: Research on the relationship between interdisciplinarity and academic influence of literature under different collaboration modes. J. Intelligence 40(8), 164–172 (2021)

    Google Scholar 

  3. Sang, G., Han, X.: The equilibrium property in scientific collaborations. J. Univ. Elec. Sci. Technol. China 48(5), 786–793 (2019)

    Google Scholar 

  4. He, C., Wu, J., Wei, Z., et al.: Research dominance between institutions and its proximity mechanism in research collaboration: a case study of China’s biomedical field. J. China Soc. Sci. Tech. Info. 39(2), 148–157 (2020)

    Google Scholar 

  5. Chen, X., Zhang, Z.: Article title. A Review of the Impact of International Collaboration on Research Performance 63(15), 127–139 (2019)

    MathSciNet  Google Scholar 

  6. Cui, B., Dong, K., Xu, H.: Research on the influence and action path of patent cooperation on technology transfer: a case study of a scientific research institution of China. Info. Stud. Theory & Application 43(12), 103–110 (2020)

    Google Scholar 

  7. Zhang, Y., Long, M., Zhu, G.: The influence of Chinese research university’s participation in industry-university-research cooperation on academic performance from the perspective of research team. Sci. Technol. Progr. Poli. 36(1), 132–141 (2019)

    Google Scholar 

  8. Gui, Q., Liu, C., Du, D.: The structure and dynamic of scientific collaboration network among countries along the belt and road. Sustainability 11(19), 5187 (2019)

    Article  Google Scholar 

  9. Liu, F., Mao, J., Li, G.: The characteristics of scientific research collaboration and spatial agglomeration pattern in the field of scientometrics. Information Science 40(1), 166–175 (2022)

    Google Scholar 

  10. Wang, L., Wang, X., Philipsen, N.J.: Network structure of scientific collaborations between China and the EU member states. Scientometrics 113(2), 765–781 (2017). https://doi.org/10.1007/s11192-017-2488-6

    Article  Google Scholar 

  11. Feng, S., Kirkley, A.: Mixing patterns in interdisciplinary co-authorship networks at multiple scales. Scientific Reports (10), 7731 (2020)

    Google Scholar 

  12. Li, Y., Li, H., Liu, N., Liu, X.: Important institutions of interinstitutional scientific collaboration networks in materials science. Scientometrics 117(1), 85–103 (2018). https://doi.org/10.1007/s11192-018-2837-0

    Article  Google Scholar 

  13. Wen, P., Zheng, X., Lai, T., et al.: Research on the science research relationship and development trend of NSF institutions in US. Journal of Modern Information 41(4), 154–161 and 177 (2021)

    Google Scholar 

  14. Zhou, J., Zeng, A., Fan, Y., Di, Z.: Identifying important scholars via directed scientific collaboration networks. Scientometrics 114(3), 1327–1343 (2017). https://doi.org/10.1007/s11192-017-2619-0

    Article  Google Scholar 

  15. Yu, C., Lin, A., Zhong, Y., et al.: Scientific collaboration recommendation based on network embedding. J. China Soc. Sci. Techni. Info. 38(5), 500–511 (2019)

    Google Scholar 

  16. Sarajlić, A., Malod-Dognin, N., Yaveroğlu, Ö.N., et al.: Graphlet-based characterization of directed networks. Sci. Rep. 6(1), 35098 (2016)

    Article  Google Scholar 

  17. Milo, R., Shen-Orr, S., Itzkovitz, S., et al.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)

    Article  Google Scholar 

  18. Dey, A.K., Gel, Y.R., Poor, H.V.: What network motifs tell us about resilience and reliability of complex networks. Proc. Natl. Acad. Sci. 116(39), 19368–19373 (2019)

    Article  Google Scholar 

  19. Tamara, D., Kristijan, P., Ljupcho, K.: Graphlets in Multiplex Networks. Scientific Reports 10(1), 1–13 (2020)

    Google Scholar 

  20. Yan, W., Wen, X.: Following and cooperation: an analysis of online academic relationship networks and behavior patterns of research users. Info. Stud. Theory & Application 45(4), 75–82 (2022)

    Google Scholar 

  21. Han, T., Wang, L., Xu, X.: Evolution of scientific research cooperation network in the yangtze river delta city group: empirical analysis based on SCIE and SSCI papers. Info. Stud. Theory & Application 43(10), 151–156 (2020)

    Google Scholar 

  22. Huang, L., Ni, X., Cheng, K., et al.: Identification of potential research partners based on two-mode network analysis. J. China Soc. Sci. Techni. Info. 39(9), 906–913 (2020)

    Google Scholar 

  23. Lin, Y., Wang, K., Liu, H., et al.: Application of network representation learning in the prediction of scholar academic cooperation. J. China Soc. Sci. Techni. Info. 39(04), 367–373 (2020)

    Google Scholar 

  24. Pelacho, M., Ruiz, G., Sanz, F., Tarancón, A., Clemente-Gallardo, J.: Analysis of the evolution and collaboration networks of citizen science scientific publications. Scientometrics 126(1), 225–257 (2020). https://doi.org/10.1007/s11192-020-03724-x

    Article  Google Scholar 

  25. Tantardini, M., Ieva, F., Tajoli, L., et al.: Comparing methods for comparing networks. Sci. Rep. 9(1), 17557 (2019)

    Article  Google Scholar 

  26. Pržulj, N.: Biological network comparison using graphlet degree distribution. Bioinformatics 23(2), 177–183 (2007)

    Article  Google Scholar 

  27. Schieber, T.A., Carpi, L., Díaz-Guilera, A., et al.: Quantification of network structural dissimilarities. Nature Communications 8, 13928 (2017)

    Google Scholar 

  28. Du, Z., Yu, S., Luo, H., et al.: Consensus convergence in large-group social network environment: Coordination between trust relationship and opinion similarity. Knowl.-Based Syst. 217, 106828 (2021)

    Article  Google Scholar 

  29. Perez, C., Ting, I.H.: Can you hold an advantageous network position? The role of neighborhood similarity in the sustainability of structural holes in social networks. Decision Support Systems 113783 (2022)

    Google Scholar 

  30. Benson, A.R., Gleich, D.F., Leskovec, J.: Higher-order organization of complex networks. Science 353(6295), 163–166 (2016)

    Article  Google Scholar 

  31. Fang, J., Zhang, P., Zhou, Y., et al.: Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer’s disease. Nature Aging 1(12), 1175–1188 (2021)

    Article  Google Scholar 

  32. Calderone, A., Formenti, M., Aprea, F., et al.: Comparing Alzheimer’s and Parkinson’s diseases networks using graph communities structure. BMC Syst. Biol. 10(1), 25 (2016)

    Article  Google Scholar 

  33. Zhao, W., Luo, J., Fan, T., et al.: Analyzing and visualizing scientific research collaboration network with core node evaluation and community detection based on network embedding. Pattern Recogn. Lett. 144, 54–60 (2021)

    Article  Google Scholar 

  34. Duan, D., Xia, Q.: Evolution of scientific collaboration on COVID-19: A bibliometric analysis. Learned Publishing 34(3), 429–441 (2021)

    Article  MathSciNet  Google Scholar 

  35. Wang, Y., Yang, X., Yu, H., et al.: The collaboration pattern and comparative analysis of research teams in the artificial intelligence field. Journal 64(20), 14–22 (2020)

    Google Scholar 

  36. Zhang, M., Ge, S., Jia, Y., et al.: Analysis of cohesion characteristics of research cooperation network based on K-core. Systems Engineering –Theory & Practice 40(7), 1821–1831 (2020)

    Google Scholar 

  37. Zhang, L., Tian, D., Qu, J.: Article title. Journal of the China Society for Scientific and Technical Information 39(7), 719–730 (2020)

    Google Scholar 

  38. Kivelä, M., Arenas, A., Barthelemy, M., et al.: Multilayer networks. Journal of complex networks 2(3), 203–271 (2014)

    Article  Google Scholar 

  39. Shi, J., Yang, R., Jin, T., et al.: Realtime top-k personalized pagerank over large graphs on GPUs. Proceedings of the VLDB Endowment 13(1), 15–28 (2019)

    Article  Google Scholar 

  40. Pržulj, N., Corneil, D.G., Jurisica, I.: Modeling interactome: scale-free or geometric? Bioinformatics 20(18), 3508–3515 (2004)

    Article  Google Scholar 

  41. Serratosa, F.: Graph edit distance: restrictions to be a metric. Pattern Recogn. 90, 250–256 (2019)

    Article  Google Scholar 

Download references

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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Authors and Affiliations

  1. Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China

    Shengqing Li & Jiulei Jiang

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  1. Shengqing Li
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  2. Jiulei Jiang
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Corresponding author

Correspondence to Shengqing Li .

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Editors and Affiliations

  1. Nanjing Institute of Technology, Nanjing, China

    Yuan Tian

  2. Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Tinghuai Ma

  3. Chinese Academy of Sciences, Shenzhen, China

    Qingshan Jiang

  4. Nanjing University of Information Science and Technology, Nanjing, China

    Qi Liu

  5. King Saud University, Riyadh, Saudi Arabia

    Muhammad Khurram Khan

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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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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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3299-3

  • Online ISBN: 978-981-99-3300-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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