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Will Affiliation Diversity Promote the Disruptiveness of Papers in Artificial Intelligence?

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Wisdom, Well-Being, Win-Win (iConference 2024)

Part of the book series: Lecture Notes in Computer Science ((volume 14597))

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Abstract

This study investigates the causal relationship between affiliation diversity and the disruptiveness of papers in the field of Artificial Intelligence (AI). We obtained 646,100 AI-related papers with complete affiliation information between 1950 and 2019 from the Microsoft Academic Graph. Descriptive analysis and Propensity Score Matching (PSM) methods are employed in this study. The results show that homophily (over 70%) has still been prevalent over the past 70 years among multi-affiliation collaborations in AI, despite the average affiliation diversity exhibiting startling upward trends when AI steps into the deep learning stage. Affiliation diversity cannot promote the disruptiveness of AI papers. On the contrary, AI papers with affiliation diversity can be 1.75% less disruptive compared to AI papers that collaborated by similar affiliations.

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Acknowledgment

This work was supported by Science Foundation of the Ministry of Education of China (Grant No. 22YJC870014), National Natural Science Foundation of China (Grant No.72204090).

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Authors and Affiliations

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

    Xuli Tang & Ming Yi

  2. School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Xin Li

Authors
  1. Xuli Tang
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  2. Xin Li
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  3. Ming Yi
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Corresponding author

Correspondence to Xin Li .

Editor information

Editors and Affiliations

  1. iSchool organization, Berlin, Germany

    Isaac Sserwanga

  2. University of Tsukuba, Tsukuba, Japan

    Hideo Joho

  3. Jilin University, Changchun, China

    Jie Ma

  4. Stockholm University, Kista, Sweden

    Preben Hansen

  5. Wuhan University, Wuhan, China

    Dan Wu

  6. University of Tsukuba, Tsukuba, Japan

    Masanori Koizumi

  7. University of California, Los Angeles, CA, USA

    Anne J. Gilliland

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Tang, X., Li, X., Yi, M. (2024). Will Affiliation Diversity Promote the Disruptiveness of Papers in Artificial Intelligence?. In: Sserwanga, I., et al. Wisdom, Well-Being, Win-Win. iConference 2024. Lecture Notes in Computer Science, vol 14597. Springer, Cham. https://doi.org/10.1007/978-3-031-57860-1_27

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  • DOI: https://doi.org/10.1007/978-3-031-57860-1_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57859-5

  • Online ISBN: 978-3-031-57860-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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