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Title: Mining Hot Research Topics based on Complex Network Analysis - A Case Study on Regenerative Medicine
Author: Ceng RQ(曾荣强)1,3; Pang HS(庞弘燊)5; Tan XC(覃筱楚)2; Song YB(宋亦兵)2; Wen Y(文奕)1; Hu ZY(胡正银)1; Yang N(杨宁)1; Guo HM(郭红梅)4; Qian L(钱力)4
Issued Date: 2017-11
Conference Name: the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Conference Date: 2017.11.1-2017.11.3
Conference Place: 葡萄牙丰沙尔
Keyword: Hot Research Topics ; Modularity Function ; Regenerative Medicine ; Community Detection ; Hypervolume Indicator
DOI: 10.5220/0006504802630268
Corresponding Author: 胡正银
English 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.
Language: 英语
Citation statistics:
Content Type: 会议论文
URI: http://ir.las.ac.cn/handle/12502/9600
Appears in Collections:中国科学院成都文献情报中心_信息技术部_会议论文

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description.institution: 1.中国科学院成都文献情报中心
2.中国科学院广州生物医药与健康研究院
3.西南交通大学
4.中国科学院文献情报中心
5.深圳大学

Recommended Citation:
Ceng RQ,Pang HS,Tan XC,et al. Mining Hot Research Topics based on Complex Network Analysis - A Case Study on Regenerative Medicine[C]. 见:the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. 葡萄牙丰沙尔. 2017.11.1-2017.11.3.
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