首页|A statistical feature data mining framework for constructing scholars’ career trajectories in academic data
A statistical feature data mining framework for constructing scholars’ career trajectories in academic data
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NSTL
Elsevier
Temporal and spatial information about scholars can be found in their academic papers. Examining the footprints of scholars’ careers can help us understand the course of their growth, potential collaborations for future research, and trends in the flow of talent. Although a great deal of research has been conducted in related fields, the challenge of accurately constructing scholars’ career trajectories from redundant and noisy academic data is far from resolved. To address this problem, a unified framework called ATrajRN that employs AMiner academic data is proposed for the first time. To accurately obtain scholars’ geographic location information from their research achievements, this study introduces an algorithm called Positioning based on Academic Achievements of Scholars (PAAS), which aims to make the most of academic data and the characteristics of different maps. To avoid the interference of data redundancy, this paper proposes a statistical feature-based method to find the most reliable career trajectories by some state-of-the-art approaches. To restore the continuously scholars’ career trajectories, this paper offers the trajectory generation algorithm based on the output from the previous step. Experiments and systematic analysis shows that the proposed novel method could achieve approximately 80% accuracy – an increase of approximately 10% – manifestly outperform the baseline method. Lastly, based on this work, we develop a system for understanding scholars’ trajectories through analysis and visualization, and we investigate the migration characteristics of typical scholar groups.
ATrajRNDeep learningNoisy dataRedundant data
Shao Z.、Wang Y.、Yuan S.、Xu J.
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School of Computer Science and Engineering Nanjing University of Science and Technology
Department of Computer Science and Technology Tsinghua University
NSTL
Elsevier
