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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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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