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CAGAT: centrality-adjusted graph attention network for active scientific talent discovery

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Abstract

Scientific talents are the cornerstone of science and technology development. The current method to find out the scientific talent is almost based on the scientists’ achievement, less considering the interrelationships hidden in the objects. In this paper, we propose a centrality-adjusted graph attention network to discover active scientific talents. This graph network can find out the internal correlation among scientific papers by using a predicate-based attention mechanism and in-degree adjustment strategy on the node. We adopt two training ways for experimentation and validation of the proposed model: in-domain and out-of-domain estimation. The experiment result shows that our model performs better than existing models on the Database of Chinese Science Citation on Normalized Discounted Cumulative Gain (NDCG) metrics.

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References

  1. Arney C 2012 Networks: An Introduction[J]. Mathematics & Computer Education 46

  2. Brin S (1998) The PageRank citation ranking: bringing order to the web[J]. Proc ASIS 1998(98):161–172

    Google Scholar 

  3. Chengwei Gu (2015) Analysis of local policies for introducing overseas scientific and technological talents in China in the new era[J]. Sci Res Manag 36(S1):272–278

    Google Scholar 

  4. Li Chong, Wang Yuchen, Du Weijing, He Xiaotao, Liu Xuemin, Zhang Shipo, Li Shuren 2021 Web of Science-based PageRank talent mining algorithm [J/OL]. Computer Applications:1–7 04–29 http://kns.cnki.net/kcms/detail/51.1307.TP.20201209.1623.020.html.

  5. Ester M, Kriegel H P, Sander J, et al 1996 A density-based algorithm for discovering clusters in large spatial databases with noise [C]. //Kdd 96 34 226 231

  6. Gardner MW, Dorling SR (1998) Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences[J]. Atmos Environ 32(14–15):2627–2636

    Article  Google Scholar 

  7. Gharebagh SS, Rostami P, Neshati M 2018 T-shaped mining: a novel approach to talent finding for agile software teams[C]//European conference on information retrieval. Springer, Cham 411–423.

  8. Grover A, Leskovec J 2016 node2vec: Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining 855–864

  9. Jantan H, Hamdan AR, Othman ZA (2009) Potential data mining classification techniques for academic talent forecasting. Ninth Int Conf Int Syst Des Appl 2009:1173–1178

    Google Scholar 

  10. Ling F, Expo X, Bin L (2019) A multilayer perceptron-based talent discovery method for technological innovation[J]. Comput Appl Softw 36(07):26–31

    Google Scholar 

  11. Michael 2009 DBLP: some lessons learned[J]

  12. Moreira C 2011 Learning to rank academic experts [D]. Instituto Superior Tecnico 30–49

  13. Moreira C, Calado P, Martins B (2015) Learning to rank academic experts in the DBLP dataset[J]. Expert Syst 32(4):477–493

    Article  Google Scholar 

  14. Osisanwo FY, Akinsola JET, Awodele O et al (2017) Supervised machine learning algorithms: classification and comparison[J]. Int J Comput Trends Technol 48(3):128–138

    Article  Google Scholar 

  15. Park N, Kan A, Dong X L, et al 2019 Estimating node importance in knowledge graphs using graph neural networks[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 596 606

  16. J Ren, Wang L, Wang K, et al 2019 API an index for quantifying a scholar’s academic potential[J] IEEE Access 7 178675 178684

  17. Ruixia X, Xiuxia Li (2019) Author influence evaluation index based on co-authorship network[J]. Int Theory Pract 42(01):100–104

    Google Scholar 

  18. Shi C, Xu C, Yang X (2009) Study of TFIDF algorithm[J]. J Comput Appl 29(6):167–170

    Google Scholar 

  19. Vaswani A, Shazeer N, Parmar N, et al 2017 Attention is all you need[J]. arXiv preprint 1–4

  20. Veličković P, Cucurull G, Casanova A, et al. Graph attention networks[J]. arXiv preprint 2017: 1–5

  21. Wu Y, Sun Y, Zhuang F et al 2020 Meta-path hierarchical heterogeneous graph convolution network for high potential scholar recognition[C]// 2020 IEEE International Conference on Data Mining (ICDM). IEEE

  22. Yanping J, Wanjun X, Yingmei Z, Xiaohong C (2019) A study on the discovery and evaluation strategy of global potential Chinese young scholars based on bibliometric method[J]. J Intelligence 38(07):178–183

    Google Scholar 

  23. Ye Y, Zhu H, Xu T, et al 2019 Identifying high potential talent: a neural network based dynamic social profiling approach[C]//2019 IEEE International Conference on Data Mining (ICDM). IEEE 718–727.

  24. Yilmaz E, Kanoulas E, Aslam JA 2008 A simple and efficient sampling method for estimating AP and NDCG[C]//Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval 603 610

  25. Zhao, Ning, Guohui Yang, and Yang Cao 2020 Mining technological innovation talents based on patent index using t-SNE algorithms*: take the field of intelligent robot as an example. 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE 595–601.

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Authors and Affiliations

  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Chong Li, Jinjie Zhang, Yuchen Wang & Xuemin Liu

  2. University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China

    Jinjie Zhang & Yuchen Wang

Authors
  1. Chong Li
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  2. Jinjie Zhang
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  3. Yuchen Wang
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  4. Xuemin Liu
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Correspondence to Jinjie Zhang.

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Li, C., Zhang, J., Wang, Y. et al. CAGAT: centrality-adjusted graph attention network for active scientific talent discovery. Pers Ubiquit Comput 26, 177–184 (2022). https://doi.org/10.1007/s00779-021-01659-5

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  • DOI: https://doi.org/10.1007/s00779-021-01659-5

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