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Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research Cover

Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research

Journal of Data and Information Science
Volume 1 (2016): Issue 4 (November 2016)
By: Ling Wei,  Haiyun Xu,  Zhenmeng Wang,  Kun Dong,  Chao Wang and  Shu Fang  
Open Access
|Sep 2017

Authors

Ling Wei

weiling@mail.las.ac.cn

Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu, China
University of the Chinese Academy of Sciences, Beijing, China
School of Information Management, Shanxi University of Finance & Economics, Taiyuan, China

Haiyun Xu

xuhy@clas.ac.cn

Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu, China

Zhenmeng Wang

wangzhenmeng@mail.las.ac.cn

Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu, China
University of the Chinese Academy of Sciences, Beijing, China

Kun Dong

dongkun@mail.las.ac.cn

Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu, China
University of the Chinese Academy of Sciences, Beijing, China

Chao Wang

wangchao@mail.las.ac.cn

Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu, China
University of the Chinese Academy of Sciences, Beijing, China

Shu Fang

fangsh@clas.ac.cn

Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu, China
DOI: https://doi.org/10.20309/jdis.201626 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X
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Language: English
Page range: 81 - 101
Submitted on: May 30, 2016
Accepted on: Sep 12, 2016
Published on: Sep 1, 2017
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Research topics,
Weak tie network,
Weak tie theory,
Weak tie nodes,
Library and Information Science (LIS)
Related subjects:
Computer sciences,
Information technology,
Project management,
Databases and data mining

© 2017 Ling Wei, Haiyun Xu, Zhenmeng Wang, Kun Dong, Chao Wang, Shu Fang, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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