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Authors: Rong-Qiang Zeng 1 ; Hong-Shen Pang 2 ; Xiao-Chu Qin 3 ; Yi-Bing Song 3 ; Yi Wen 4 ; Zheng-Yin Hu 4 ; Ning Yang 4 ; Hong-Mei Guo 5 and Qian Li 5

Affiliations: 1 Chengdu Documentation and Information Center, Chinese Academy of Sciences, School of Mathematics and Southwest Jiaotong University, China ; 2 Shenzhen University, China ; 3 Guangzhou Institutes of Biomedicine and Health and Chinese Academy of Sciences, China ; 4 Chengdu Documentation and Information Center and Chinese Academy of Sciences, China ; 5 National Science Library and Chinese Academy of Sciences, China

Keyword(s): Hot Research Topics, Modularity Function, Regenerative Medicine, Community Detection, Hypervolume Indicator.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Clustering and Classification Methods ; Data Analytics ; Data Engineering ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Mining Text and Semi-Structured Data ; Symbolic Systems

Abstract: In order to mine the hot research topics of a certain field, we propose a hypervolume-based selection algorithm based on the complex network analysis, which employs a hypervolume indicator to select the hot research topics from the network in the considered field. We carry out the experiments in the field of regenerative medicine, and the experimental results indicate that our proposed method can effectively find the hot research topics in this field. The performance analysis sheds lights on the ways to further improvements.

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Paper citation in several formats:
Zeng, R. ; Pang, H. ; Qin, X. ; Song, Y. ; Wen, Y. ; Hu, Z. ; Yang, N. ; Guo, H. and Li, Q. (2017). Mining Hot Research Topics based on Complex Network Analysis - A Case Study on Regenerative Medicine. In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR; ISBN 978-989-758-271-4; ISSN 2184-3228, SciTePress, pages 263-268. DOI: 10.5220/0006504802630268

@conference{kdir17,
author={Rong{-}Qiang Zeng and Hong{-}Shen Pang and Xiao{-}Chu Qin and Yi{-}Bing Song and Yi Wen and Zheng{-}Yin Hu and Ning Yang and Hong{-}Mei Guo and Qian Li},
title={Mining Hot Research Topics based on Complex Network Analysis - A Case Study on Regenerative Medicine},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR},
year={2017},
pages={263-268},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006504802630268},
isbn={978-989-758-271-4},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR
TI - Mining Hot Research Topics based on Complex Network Analysis - A Case Study on Regenerative Medicine
SN - 978-989-758-271-4
IS - 2184-3228
AU - Zeng, R.
AU - Pang, H.
AU - Qin, X.
AU - Song, Y.
AU - Wen, Y.
AU - Hu, Z.
AU - Yang, N.
AU - Guo, H.
AU - Li, Q.
PY - 2017
SP - 263
EP - 268
DO - 10.5220/0006504802630268
PB - SciTePress

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