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Global Collaboration in Artificial Intelligence: Bibliometrics and Network Analysis from 1985 to 2019

机译:人工智能的全球合作:1985年至2019年的书法素质和网络分析

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Purpose This study aims to explore the trend and status of international collaboration in the field of artificial intelligence (AI) and to understand the hot topics, core groups, and major collaboration patterns in global AI research. Design/methodology/approach We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science (WoS) and studied international collaboration from the perspectives of authors, institutions, and countries through bibliometric analysis and social network analysis. Findings The bibliometric results show that in the field of AI, the number of published papers is increasing every year, and 84.8% of them are cooperative papers. Collaboration with more than three authors, collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns. Through social network analysis, this study found that the US, the UK, France, and Spain led global collaboration research in the field of AI at the country level, while Vietnam, Saudi Arabia, and United Arab Emirates had a high degree of international participation. Collaboration at the institution level reflects obvious regional and economic characteristics. There are the Developing Countries Institution Collaboration Group led by Iran, China, and Vietnam, as well as the Developed Countries Institution Collaboration Group led by the US, Canada, the UK. Also, the Chinese Academy of Sciences (China) plays an important, pivotal role in connecting the these institutional collaboration groups. Research limitations First, participant contributions in international collaboration may have varied, but in our research they are viewed equally when building collaboration networks. Second, although the edge weight in the collaboration network is considered, it is only used to help reduce the network and does not reflect the strength of collaboration. Practical implications The findings fill the current shortage of research on international collaboration in AI. They will help inform scientists and policy makers about the future of AI research. Originality/value This work is the longest to date regarding international collaboration in the field of AI. This research explores the evolution, future trends, and major collaboration patterns of international collaboration in the field of AI over the past 35 years. It also reveals the leading countries, core groups, and characteristics of collaboration in the field of AI.
机译:目的本研究旨在探讨人工智能(AI)领域国际合作的趋势和地位,了解全球AI研究中的热门话题,核心群体和主要协作模式。设计/方法/方法我们在1985年至2019年在科学网站(WOS)的核心收集数据库中选择了38,224篇论文,并从教学计量分析和社交网络的作者,机构和国家的角度研究了国际合作分析。调查结果表明,在AI领域,公布文件的数量每年都在增加,其中84.8%是合作论文。与三位以上的作者合作,两国之间的合作以及机构内的合作是合作模式的三个主要层面。通过社会网络分析,这项研究发现,美国,英国,法国和西班牙在国家一级的AI领域领导了全球合作研究,而越南,沙特阿拉伯和阿拉伯联合酋长国的国际参与程度很高。机构层面的合作反映了明显的区域和经济特征。由伊朗,中国和越南领导的发展中国家机构协作集团,以及由美国加拿大加拿大领导的发达国家机构协作集团。此外,中国科学院(中国)在联系这些机构协作团体方面发挥着重要的关键作用。研究限制首先,国际合作的参与者贡献可能有所不同,但在我们的研究中,在建立协作网络时,它们同样地看。其次,虽然考虑了协作网络中的边缘重量,但它仅用于帮助减少网络并且不反映协作的强度。实际意义调查结果填补了目前AI国际合作研究的短缺。他们将帮助科学家和政策制定者通知AI研究的未来。原创性/价值这项工作是关于AI领域的国际合作的最长。这项研究在过去35年里探讨了AI领域的进展,未来趋势和国际合作的主要合作模式。它还揭示了领先国家,核心群体和AI领域合作的特征。

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