Abstract
Regarding the fierce competition between research institutions, institutional rankings are widely carried out. At present, there are many factors affecting the ranking of institutions, but most of them are aimed at the attributes of the institutions themselves, and the feature selection is relatively simple. Therefore, this paper proposes a state-of-the-art method combining different types of features for predicting the influence of scientific research institutions. Based on the MAG dataset, this paper first calculates the institutional scores through the publication volume of the article, constructs an inter-institutional cooperation network, and calculates the importance characteristics of the institutions in the network. Then, considering the contribution of the faculty and staff to the organization, an individual characteristic based on the author’s influence is constructed. Finally, a random forest algorithm is used to solve this prediction problem. As a result, this paper raises the ranking accuracy rate NDCG@20 to 0.865, which is superior to other methods. The experimental results show that this method has a good effect on the prediction of innovation capability.
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Cuthbert, R.: University rankings, diversity, and the new landscape of higher education. Int. J. Lifelong Educ. 30, 119–121 (2011)
Szentirmai, L., Radacs, L.: World university rankings qualify teaching and primarily research. In: IEEE International Conference on Emerging Elearning Technologies and Applications, pp. 369–374 (2013)
Sinha, A., et al.: An overview of microsoft academic service (MAS) and applications. In: International Conference on World Wide Web, pp. 243–246 (2015)
Mussard, M., James, A.P.: Engineering the global university rankings: gold standards, its limitations and implications. IEEE Access PP, 1 (2018)
Al-Juboori, A.F.M.A., Su, D.J., Ko, F.: University ranking and evaluation: trend and existing approaches. In: The International Conference on Next Generation Information Technology, pp. 137–142 (2011)
Gupta, A., Murty, M.N.: Finding influential institutions in bibliographic information networks (2016)
Orouskhani, Y., Tavabi, L.: Ranking research institutions based on related academic conferences. arXiv e-prints (2016)
Wilson, J., Mohan, R., Arif, M., Chaudhury, S., Lall, B.: Ranking academic institutions on potential paper acceptance in upcoming conferences (2016)
Sandulescu, V., Chiru, M.: Predicting the future relevance of research institutions - the winning solution of the KDD Cup 2016 (2016)
Zhang, J., Xu, B., Liu, J., Tolba, A., Al-Makhadmeh, Z., Xia, F.: PePSI: Personalized prediction of scholars’ impact in heterogeneous temporal academic networks (2018)
Klimek, P.S., Jovanovic, A., Egloff, R., Schneider, R.J.S.: Successful fish go with the flow: citation impact prediction based on centrality measures for term–document networks. Scientometrics 107(3), 1265-1282 (2016)
Xie, J.: Predicting institution-level paper acceptance at conferences: a time-series regression approach (2016)
Qian, Y., Dong, Y., Ma, Y., Jin, H., Li, J.: Feature engineering and ensemble modeling for paper acceptance rank prediction (2016)
Moed, H.: Bibliometric rankings of world universities (2006)
Bai, X., Zhang, F., Hou, J., Xia, F., Tolba, A., Elashkar, E.: Implicit multi-feature learning for dynamic time series prediction of the impact of institutions. IEEE Access PP, 1 (2017)
Crucitti, P., Latora, V., Marchiori, M., Rapisarda, A.: Error and attack tolerance of complex networks. Nature 340, 378–382 (2000)
Holme, P., Edling, C.R., Liljeros, F.: Structure and time evolution of an Internet dating community. Soc. Netw. 26, 155–174 (2004)
Barabási, A.L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Physica A Stat. Mech. Appl. 311, 590–614 (2002)
Ren, X., Lü, L.: Review of ranking nodes in complex networks. Chin. Sci. Bull. 59, 1175 (2014)
Belgiu, M., Drăguţ, L.: Random forest in remote sensing: a review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 114, 24–31 (2016)
Désir, C., Bernard, S., Petitjean, C., Heutte, L.: One class random forests. Pattern Recogn. 46, 3490–3506 (2013)
Zhou, Z.H.: Ensemble learning. In: Encyclopedia of Biometrics, pp. 270–273 (2009)
Wang, Y., Wang, L., Li, Y., He, D., Liu, T.Y., Chen, W.: A theoretical analysis of NDCG type ranking measures. J. Mach. Learn. Res. 30, 25–54 (2013)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (Nos. 61472282, 61672035, and 61872004), Anhui Province Funds for Excellent Youth Scholars in Colleges (gxyqZD2016068), the fund of Co-Innovation Center for Information Supply & Assurance Technology in AHU (ADXXBZ201705), and Anhui Scientific Research Foundation for Returned Scholars.
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Wang, W., Wang, G., Zhang, J., Chen, P., Wang, B. (2019). Ranking Research Institutions Based on the Combination of Individual and Network Features. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_41
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DOI: https://doi.org/10.1007/978-3-030-26766-7_41
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