{"title":"高产科研团队演化量化指标的比较研究","authors":"Bentao Zou , Yuefen Wang","doi":"10.2478/dim-2020-0028","DOIUrl":null,"url":null,"abstract":"<div><p>Scientific research teams play an increasingly significant role in scientific activities. To better understand the dynamic evolution process of research teams, we explored measures that quantify the evolution of prolific research teams. We collected our data from the Web of Science in the field of artificial intelligence, and applied the label propagation algorithm to identify research teams in the co-authorship network. The Top 1‰ prolific teams were selected as our research object, whose node stability and two types of edge stabilities were measured. The results show that prolific teams are much more stable during the evolution process, in terms of both member and membership stability. The measure of stability has varying degrees of impact on teams with different sizes, and small-sized teams get considerably different stability results by different measures.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"5 1","pages":"Pages 56-64"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2543925122000213/pdfft?md5=f37cf316a71f0ad36d20545073f1e4b9&pid=1-s2.0-S2543925122000213-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A Comparison Study of Measures to Quantify the Evolution of Prolific Research Teams\",\"authors\":\"Bentao Zou , Yuefen Wang\",\"doi\":\"10.2478/dim-2020-0028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Scientific research teams play an increasingly significant role in scientific activities. To better understand the dynamic evolution process of research teams, we explored measures that quantify the evolution of prolific research teams. We collected our data from the Web of Science in the field of artificial intelligence, and applied the label propagation algorithm to identify research teams in the co-authorship network. The Top 1‰ prolific teams were selected as our research object, whose node stability and two types of edge stabilities were measured. The results show that prolific teams are much more stable during the evolution process, in terms of both member and membership stability. The measure of stability has varying degrees of impact on teams with different sizes, and small-sized teams get considerably different stability results by different measures.</p></div>\",\"PeriodicalId\":72769,\"journal\":{\"name\":\"Data and information management\",\"volume\":\"5 1\",\"pages\":\"Pages 56-64\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2543925122000213/pdfft?md5=f37cf316a71f0ad36d20545073f1e4b9&pid=1-s2.0-S2543925122000213-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data and information management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2543925122000213\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data and information management","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2543925122000213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
科研团队在科学活动中发挥着越来越重要的作用。为了更好地理解科研团队的动态演化过程,我们探索了量化高产科研团队演化的方法。我们从人工智能领域的Web of Science中收集数据,并应用标签传播算法在合作作者网络中识别研究团队。选取高产率前1‰的团队作为研究对象,对其节点稳定性和两种边缘稳定性进行了测量。结果表明,在进化过程中,多产的团队在成员和成员稳定性方面都更加稳定。稳定性的度量对不同规模的团队有不同程度的影响,小型团队通过不同的度量得到的稳定性结果差异较大。
A Comparison Study of Measures to Quantify the Evolution of Prolific Research Teams
Scientific research teams play an increasingly significant role in scientific activities. To better understand the dynamic evolution process of research teams, we explored measures that quantify the evolution of prolific research teams. We collected our data from the Web of Science in the field of artificial intelligence, and applied the label propagation algorithm to identify research teams in the co-authorship network. The Top 1‰ prolific teams were selected as our research object, whose node stability and two types of edge stabilities were measured. The results show that prolific teams are much more stable during the evolution process, in terms of both member and membership stability. The measure of stability has varying degrees of impact on teams with different sizes, and small-sized teams get considerably different stability results by different measures.