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A hybrid clustering approach to identify network positions and roles through social network and multivariate analysis

Author

Listed:
  • Yu-Hsin Chang

    (Chaoyang University of Technology)

  • Kuei-Kuei Lai

    (Chaoyang University of Technology)

  • Chien-Yu Lin

    (National Yunlin University of Science and Technology)

  • Fang-Pei Su

    (Chaoyang University of Technology)

  • Ming-Chung Yang

    (National Chin-Yi University of Technology)

Abstract

This study proposes a hybrid clustering approach to identify the positions and roles in a relational network by integrating multivariate and social network analysis. First, an adjacency matrix was constructed based on the graph theory to indicate the relation between the collected data. Next, network analysis was conducted and the statistics of network centrality as clustering variables were computed. After, this study reduced clustering variables using the principal component analysis. These selected principal components were then used as clustering variables for a two-step cluster analysis. Hierarchical cluster analysis was first made to determine the appropriate number of clusters and then K-means clustering was used for dividing actors into k proper positions. In addition, the multivariate analysis of variance was conducted to test the significance between those positions. After, a new adjacency matrix was built upon the rearrangement of k positions. The frequency within and between these positions was computed and the cut-off value was determined to distinguish the difference between these frequencies. Finally, each position was labeled based on its characteristics and the relationships within and between these positions. After the structured approach was established, the litigation-related network of smartphone makers was used as empirical evidence. The results showed that this structured approach can effectively distinguish the position and role of a company in a relational network.

Suggested Citation

  • Yu-Hsin Chang & Kuei-Kuei Lai & Chien-Yu Lin & Fang-Pei Su & Ming-Chung Yang, 2017. "A hybrid clustering approach to identify network positions and roles through social network and multivariate analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1733-1755, December.
    • Handle: RePEc:spr:scient:v:113:y:2017:i:3:d:10.1007_s11192-017-2556-y
    DOI: 10.1007/s11192-017-2556-y
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    References listed on IDEAS

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    Cited by:

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    2. Kuang-Cheng Chai & Yang Yang & Zhiyong Sui & Ke-Chiun Chang, 2020. "Determinants of highly-cited green patents: The perspective of network characteristics," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-13, October.
    3. Mohd Shadab Danish & Pritam Ranjan & Ruchi Sharma, 2022. "Assessing the Impact of Patent Attributes on the Value of Discrete and Complex Innovations," Papers 2208.07222, arXiv.org.
    4. Daim, Tugrul & Lai, Kuei Kuei & Yalcin, Haydar & Alsoubie, Fayez & Kumar, Vimal, 2020. "Forecasting technological positioning through technology knowledge redundancy: Patent citation analysis of IoT, cybersecurity, and Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    5. Mohd Shadab Danish & Pritam Ranjan & Ruchi Sharma, 2021. "Identification of “Valuable” Technologies via Patent Statistics in India: An Analysis Based on Renewal Information," BASE University Working Papers 13/2021, BASE University, Bengaluru, India.

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