A statistical feature data mining framework for constructing scholars’ career trajectories in academic data-Reference-Cited by-同舟云学术

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A statistical feature data mining framework for constructing scholars’ career trajectories in academic data

Published:2022-03 Issue: Volume:118 Page:108550
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Shao Zhou^ORCID,Yuan Sha,Xu Jing,Wang Yongli

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Software

Reference43 articles.

1. Science behind AI: the evolution of trend, mobility, and collaboration;Yuan;Scientometrics,2020

2. Evolutions and trends of artificial intelligence (AI): research, output, influence and competition;Shao;Library Hi Tech,2021

3. Where have you been? Inferring career trajectory from academic social network;Wu,2018

4. Scholar trajectory: Visualizing the migration of talents;Shao,2018

5. Elad: An entity linking based affiliation disambiguation framework;Shao;IEEE Access,2020

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Scholar's Career Switch from Academia to Industry: Mining and Analysis from AMiner;Big Data Research;2024-05

2. Exploring Emerging Researchers in LATAM: A Comparative Study with Elsevier Data and Machine Learning Techniques;2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE);2023-07-24

3. A multi-channel multi-tower GNN model for job transfer prediction based on academic social network;Applied Soft Computing;2023-07

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