| 追踪物理学中的跨领域模因 |
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| 引用本文： | 周毅,闫光辉,卢彬炜,王珊,李世魁,卫祥,杨仕博,靳丹.追踪物理学中的跨领域模因[J].应用科学学报,2021,39(3):387-386. |
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| 作者姓名： | 周毅 闫光辉 卢彬炜 王珊 李世魁 卫祥 杨仕博 靳丹 |
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| 作者单位： | 1. 兰州交通大学 电子与信息工程学院, 甘肃 兰州 730070;2. 甘肃同兴智能科技发展有限责任公司, 甘肃 兰州 730050;3. 国网甘肃省电力公司 信息通信公司, 甘肃 兰州 730050 |
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| 基金项目： | 国家自然科学基金（No.62062049）；教育部人文社会科学研究基金（No.20YJCZH212）；甘肃省自然科学基金（No.20JR5RA390）资助 |
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| 摘 要： | 以美国物理学会旗下期刊2000—2019年发表的论文和Web of Science论文摘要为基础，用模因短语刻画知识，构建模因关系网络并引入跨学科测度Rao-Stirling指数以计算模因的跨领域分数，从而追踪物理学中的跨领域模因。分别从网络拓扑结构指标、跨领域测度指标和专业术语对比3个方面进行验证，证明了所提的模因关系网络和模因的跨领域分数可以有效反映知识在不同领域间的扩散现象。
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| 关 键 词： | 模因 跨领域 社区发现 美国物理学会数据集 |
| 收稿时间： | 2020-08-26 |
Tracking Interdisciplinary Memes in Physics |
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| ZHOU Yi,YAN Guanghui,LU Binwei,WANG Shan,LI Shikui,WEI Xiang,YANG Shibo,JIN Dan.Tracking Interdisciplinary Memes in Physics[J].Journal of Applied Sciences,2021,39(3):387-386. |
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| Authors: | ZHOU Yi YAN Guanghui LU Binwei WANG Shan LI Shikui WEI Xiang YANG Shibo JIN Dan |
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| Institution: | 1. School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China;2. Gansu Tongxing Intelligent Technology Development Company, Lanzhou 730050, Gansu, China;3. Information and Telecommunication Company, State Grid Gansu Electric Power Company, Lanzhou 730050, Gansu, China |
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| Abstract: | Based on the papers published in American Physical Society (APS) journals from 2000 to 2019 and abstracts from Web of Science, we firstly use meme phrases to describe knowledge and construct meme correlation network (MCN) to track interdisciplinary memes. And then we use Rao-Stirling index to calculate the interdisciplinary scores of memes, and track interdisciplinary memes in physics. The verification experiment was carried out from three perspectives of network topology index, interdisciplinary measurement index and professional terminology comparison. It proved that the meme correlation network (MCN) and the interdisciplinary score of memes in this paper can effectively reflect the diffusion of knowledge in different domains. |
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| Keywords: | meme interdisciplinary community detection American Physical Society (APS) data set |
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