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Multi-modal social networks for modeling scientific fields

Author

Listed:
  • Georg Groh

    (TU München)

  • Christoph Fuchs

    (TU München)

Abstract

This paper analyzes whether methods from social network analysis can be adopted for the modeling of scientific fields in order to obtain a better understanding of the respective scientific area. The approach proposed is based on articles published within the respective scientific field and certain types of nodes deduced from these papers, such as authors, journals, conferences and organizations. As a proof of concept, the techniques discussed here are applied to the field of ‘Mobile Social Networking’. For this purpose, a tool was developed to create a large data collection representing the aforementioned field. The paper analyzes various views on the complete network and discusses these on the basis of the data collected on Mobile Social Networking. The authors demonstrate that the analysis of particular subgraphs derived from the data collection allows the identification of important authors as well as separate sub-disciplines such as classic network analysis and sensor networks and also contributes to the classification of the field of ‘Mobile Social Networking’ within the greater context of computer science, applied mathematics and social sciences. Based on these results, the authors propose a set of concrete services which could be offered by such a network and which could help the user to deal with the scientific information process. The paper concludes with an outlook upon further possible research topics.

Suggested Citation

  • Georg Groh & Christoph Fuchs, 2011. "Multi-modal social networks for modeling scientific fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 569-590, November.
    • Handle: RePEc:spr:scient:v:89:y:2011:i:2:d:10.1007_s11192-011-0475-x
    DOI: 10.1007/s11192-011-0475-x
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    2. Rongying Zhao & Bikun Chen, 2014. "Applying author co-citation analysis to user interaction analysis: a case study on instant messaging groups," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 985-997, November.
    3. 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.
    4. Jun-Ping Qiu & Ke Dong & Hou-Qiang Yu, 2014. "Comparative study on structure and correlation among author co-occurrence networks in bibliometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1345-1360, November.
    5. Guijie Zhang & Luning Liu & Fangfang Wei, 2019. "Key nodes mining in the inventor–author knowledge diffusion network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 721-735, March.
    6. Katja Rost & Thorsten Teichert & Alan Pilkington, 2017. "Social network analytics for advanced bibliometrics: referring to actor roles of management journals instead of journal rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1631-1657, September.
    7. Guijie Zhang & Luning Liu & Yuqiang Feng & Zhen Shao & Yongli Li, 2014. "Cext-N index: a network node centrality measure for collaborative relationship distribution," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 291-307, October.
    8. Rodica Ioana Lung & Noémi Gaskó & Mihai Alexandru Suciu, 2018. "A hypergraph model for representing scientific output," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1361-1379, December.

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