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A dynamic graph-based approach to ranking firms for identifying key players using inter-firm transactions

Author

Listed:
  • Ali Tosyali

    (University of Delaware)

  • Jeongsub Choi

    (West Virginia University)

  • Byunghoon Kim

    (Hanyang University)

  • Hoshin Lee

    (Korea Institute of Science and Technology Information)

  • Myong K. Jeong

    (Rutgers University)

Abstract

Ranking firms in an inter-firm transaction network is a crucial task for identifying key players in an industry, thereby explaining the agglomeration of economic activities and assisting with competitor identification. To the best of our knowledge, despite the advantages of network-based approaches in market analysis, few studies have employed network analysis tools to rank firms. However, these studies failed to capture the characteristics of inter-firm transaction networks (i.e., evolving over time, having multiple edges between nodes, among others). In this study, we propose a new ranking method, FirmRank, that identifies key players based on centrality metrics in network analysis, leveraging inter-firm transactions to discern the characteristics of an inter-firm transaction network. Our proposed ranking method is evaluated using real-world datasets from a corporate information database, and the evaluation results demonstrate the superiority of our method over well-known ranking methods—PageRank and age-based PageRank.

Suggested Citation

  • Ali Tosyali & Jeongsub Choi & Byunghoon Kim & Hoshin Lee & Myong K. Jeong, 2021. "A dynamic graph-based approach to ranking firms for identifying key players using inter-firm transactions," Annals of Operations Research, Springer, vol. 303(1), pages 5-27, August.
    • Handle: RePEc:spr:annopr:v:303:y:2021:i:1:d:10.1007_s10479-021-04100-5
    DOI: 10.1007/s10479-021-04100-5
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    References listed on IDEAS

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    Cited by:

    1. Jeongsub Choi & Byunghoon Kim & Ho-shin Lee, 2024. "Competitor identification with memory in a dynamic financial transaction network," Annals of Operations Research, Springer, vol. 341(1), pages 349-374, October.
    2. Yi Yu & Jaeseung Baek & Ali Tosyali & Myong K. Jeong, 2024. "Robust asymmetric non-negative matrix factorization for clustering nodes in directed networks," Annals of Operations Research, Springer, vol. 341(1), pages 245-265, October.

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