Exploring time factors in measuring the scientific impact of scholars

Jun Zhang, Zhaolong Ning (), Xiaomei Bai, Xiangjie Kong, Jinmeng Zhou and Feng Xia
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Jun Zhang: Dalian University of Technology
Zhaolong Ning: Dalian University of Technology
Xiaomei Bai: Dalian University of Technology
Xiangjie Kong: Dalian University of Technology
Jinmeng Zhou: Dalian University of Technology
Feng Xia: Dalian University of Technology

Scientometrics, 2017, vol. 112, issue 3, No 8, 1321 pages

Abstract: Abstract Taking advantage of the easy access to the rich and massive scholarly data, more and more researchers are focusing on the studies of analyzing and utilizing the scholarly big data. Among them, evaluating the scientific impact of scholars has significant importance. Measuring the scientific impact of scholars can not only provide basis for the applications of academic foundations and awards, but also shed light on the research directions for scholars. Currently, citation based methods and network based metrics are the most commonly used ways to evaluate the scientific impact. However, these approaches ignore several important facts, i.e. the dynamics of citations and the initial qualities of different articles. To alleviate the shortcomings of them, we propose a Time-aware Ranking algorithm (TRank) to evaluate the impact of scholars. Due to scholars’ sustainable supreme concerns of academic innovations, the TRank algorithm gives more credits to the newly published scholarly papers as well as their references according to the representative time functions. Our method also combines the merits of random walk algorithms and heterogeneous network topology, i.e. the mutual influences among different scholarly entities in heterogeneous academic networks. To validate the suitable time function for TRank algorithm and explore its performance, we construct the experiments on two real datasets: (1) Digitial Bibliography and Library Project, and (2) American Physical Society. The experimental results demonstrate that our algorithm outperforms other methods in selecting outstanding scholars and the evaluation results on the overall impact of scholars.

Keywords: Heterogeneous network; Scholarly big data; PageRank algorithm; Random walk algorithm (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11192-017-2458-z

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