Research on influence spread of scientific research team based on scientific factor quantification of big data
With the development of science and technology, the interactions among scientific research teams become more and more frequent, and their relationship and behavior become more and more complex. Many researches mainly adopt complex network to analyze, but these researches only consider some aspects o...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-04-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719842158 |
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| _version_ | 1832547822008795136 |
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| author | Wenbin Zhao Zhixian Yin Tongrang Fan Jishuang Luo |
| author_facet | Wenbin Zhao Zhixian Yin Tongrang Fan Jishuang Luo |
| author_sort | Wenbin Zhao |
| collection | DOAJ |
| description | With the development of science and technology, the interactions among scientific research teams become more and more frequent, and their relationship and behavior become more and more complex. Many researches mainly adopt complex network to analyze, but these researches only consider some aspects of scientific research factors, so lack of comprehensive consideration. From the aspect of ability, resource, activity, and familiarity, scientific research factors are quantified based on multi-source data of scientific and technological big data, and some factors of text information are similarly quantified. Based on paper citation and project cooperation, a complex network which takes scientific research team as node is constructed and is weighted by quantification of scientific research factor. The experiment of influence spread is carried out by the comparison of unweighted network and weighted network, the comparison of single node and multiple nodes, and the comparison of influence spread and other index. The results show that the scientific research factor is closely related to the influence spread; the proposed scientific research factor quantification improves the analysis of scientific research team relationship. The relationship between influence spread and the number of related communities is greater than the number of adjacent nodes. In addition, the influence spread can effectively reflect the importance of scientific research team. |
| format | Article |
| id | doaj-art-003f60ca6cd34ac18895bb49fdde70ce |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-003f60ca6cd34ac18895bb49fdde70ce2025-02-03T06:43:08ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-04-011510.1177/1550147719842158Research on influence spread of scientific research team based on scientific factor quantification of big dataWenbin ZhaoZhixian YinTongrang FanJishuang LuoWith the development of science and technology, the interactions among scientific research teams become more and more frequent, and their relationship and behavior become more and more complex. Many researches mainly adopt complex network to analyze, but these researches only consider some aspects of scientific research factors, so lack of comprehensive consideration. From the aspect of ability, resource, activity, and familiarity, scientific research factors are quantified based on multi-source data of scientific and technological big data, and some factors of text information are similarly quantified. Based on paper citation and project cooperation, a complex network which takes scientific research team as node is constructed and is weighted by quantification of scientific research factor. The experiment of influence spread is carried out by the comparison of unweighted network and weighted network, the comparison of single node and multiple nodes, and the comparison of influence spread and other index. The results show that the scientific research factor is closely related to the influence spread; the proposed scientific research factor quantification improves the analysis of scientific research team relationship. The relationship between influence spread and the number of related communities is greater than the number of adjacent nodes. In addition, the influence spread can effectively reflect the importance of scientific research team.https://doi.org/10.1177/1550147719842158 |
| spellingShingle | Wenbin Zhao Zhixian Yin Tongrang Fan Jishuang Luo Research on influence spread of scientific research team based on scientific factor quantification of big data International Journal of Distributed Sensor Networks |
| title | Research on influence spread of scientific research team based on scientific factor quantification of big data |
| title_full | Research on influence spread of scientific research team based on scientific factor quantification of big data |
| title_fullStr | Research on influence spread of scientific research team based on scientific factor quantification of big data |
| title_full_unstemmed | Research on influence spread of scientific research team based on scientific factor quantification of big data |
| title_short | Research on influence spread of scientific research team based on scientific factor quantification of big data |
| title_sort | research on influence spread of scientific research team based on scientific factor quantification of big data |
| url | https://doi.org/10.1177/1550147719842158 |
| work_keys_str_mv | AT wenbinzhao researchoninfluencespreadofscientificresearchteambasedonscientificfactorquantificationofbigdata AT zhixianyin researchoninfluencespreadofscientificresearchteambasedonscientificfactorquantificationofbigdata AT tongrangfan researchoninfluencespreadofscientificresearchteambasedonscientificfactorquantificationofbigdata AT jishuangluo researchoninfluencespreadofscientificresearchteambasedonscientificfactorquantificationofbigdata |