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Understanding Team Collaboration in Artificial Intelligence from the Perspective of Geographic Distance

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Diversity, Divergence, Dialogue (iConference 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12645))

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Abstract

This paper analyzes team collaboration in the field of Artificial Intelligence (AI) from the perspective of geographic distance. We obtained 1,584,175 AI related publications during 1950–2019 from the Microsoft Academic Graph. Three latitude-and-longitude-based indicators were employed to quantify the geographic distance of collaborations in AI over time at domestic and international levels. The results show team collaborations in AI has been more popular in the field over time with around 42,000 (38.4%) multiple-affiliation AI publications in 2019. The changes in geographic distances of team collaborations indicate the increase of breadth and density for both domestic and international collaborations in AI over time. In addition, the United States produced the largest number of single-country and internationally collaborated AI publications, and China has played an important role in international collaborations in AI after 2010.

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Acknowledgement

This work was supported by the National Science Foundation of China (71420107026).

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Authors and Affiliations

  1. School of Information Management, Wuhan University, Wuhan, China

    Xuli Tang, Xin Li & Feicheng Ma

  2. School of Information, University of Texas at Austin, TX, USA

    Ying Ding

Authors
  1. Xuli Tang
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  2. Xin Li
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  3. Ying Ding
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  4. Feicheng Ma
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Corresponding author

Correspondence to Xin Li .

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Editors and Affiliations

  1. Humboldt University of Berlin, Berlin, Germany

    Katharina Toeppe

  2. Renmin University of China, Beijing, China

    Hui Yan

  3. The University of Hong Kong, Hong Kong, Hong Kong

    Samuel Kai Wah Chu

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Tang, X., Li, X., Ding, Y., Ma, F. (2021). Understanding Team Collaboration in Artificial Intelligence from the Perspective of Geographic Distance. In: Toeppe, K., Yan, H., Chu, S.K.W. (eds) Diversity, Divergence, Dialogue. iConference 2021. Lecture Notes in Computer Science(), vol 12645. Springer, Cham. https://doi.org/10.1007/978-3-030-71292-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-71292-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71291-4

  • Online ISBN: 978-3-030-71292-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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