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Mining diversity subgraph in multidisciplinary scientific collaboration networks: A meso perspective

Author

Listed:
  • He, Bing
  • Ding, Ying
  • Tang, Jie
  • Reguramalingam, Vignesh
  • Bollen, Johan

Abstract

This paper proposes a framework to analyze the interdisciplinary collaboration in a coauthorship network from a meso perspective using topic modeling: (1) a customized topic model is developed to capture and formalize the interdisciplinary feature; and (2) the two algorithms Diversity Subgraph Extraction (DSE) and Constraint-based Diversity Subgraph Extraction (CDSE) are designed and implemented to extract a meso view, i.e. a diversity subgraph of the interdisciplinary collaboration. The proposed framework is demonstrated using a coauthorship network in the field of computer science. A comparison between DSE and Breadth First Search (BSF)-based subgraph extraction favors DSE in capturing the diversity in interdisciplinary collaboration. Potential possibilities for studying various research topics based on the proposed framework of analysis are discussed.

Suggested Citation

  • He, Bing & Ding, Ying & Tang, Jie & Reguramalingam, Vignesh & Bollen, Johan, 2013. "Mining diversity subgraph in multidisciplinary scientific collaboration networks: A meso perspective," Journal of Informetrics, Elsevier, vol. 7(1), pages 117-128.
    • Handle: RePEc:eee:infome:v:7:y:2013:i:1:p:117-128
    DOI: 10.1016/j.joi.2012.09.005
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    References listed on IDEAS

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    Cited by:

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    2. Huimin Xu & Yi Bu & Meijun Liu & Chenwei Zhang & Mengyi Sun & Yi Zhang & Eric Meyer & Eduardo Salas & Ying Ding, 2022. "Team power dynamics and team impact: New perspectives on scientific collaboration using career age as a proxy for team power," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(10), pages 1489-1505, October.
    3. Li, Yafang & Jia, Caiyan & Yu, Jian, 2015. "A parameter-free community detection method based on centrality and dispersion of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 321-334.
    4. Li, Yafang & Jia, Caiyan & Li, Jianqiang & Wang, Xiaoyang & Yu, Jian, 2018. "Enhanced semi-supervised community detection with active node and link selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 219-232.
    5. Jing Tu, 2019. "What connections lead to good scientific performance?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 587-604, February.
    6. Xiaoling Sun & Hongfei Lin & Kan Xu & Kun Ding, 2015. "How we collaborate: characterizing, modeling and predicting scientific collaborations," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 43-60, July.
    7. Hakyeon Lee & Pilsung Kang, 2018. "Identifying core topics in technology and innovation management studies: a topic model approach," The Journal of Technology Transfer, Springer, vol. 43(5), pages 1291-1317, October.
    8. Sergi Lozano & Xosé-Pedro Rodríguez & Alex Arenas, 2014. "Atapuerca: evolution of scientific collaboration in an emergent large-scale research infrastructure," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1505-1520, February.
    9. Yongli Li & Guijie Zhang & Yuqiang Feng & Chong Wu, 2015. "An entropy-based social network community detecting method and its application to scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 1003-1017, January.
    10. Noémi Gaskó & Rodica Ioana Lung & Mihai Alexandru Suciu, 2016. "A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 613-632, August.

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