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Network mining: Applications to business data

Author

Listed:
  • Fatih Cavdur

    (Uludag University)

  • Soundar Kumara

    (Pennsylvania State University)

Abstract

This research addresses the problem of analyzing the temporal dynamics of business organizations. In particular, we concentrate on inferring the related businesses, i.e., are there groups of companies that are highly correlated through some measurement (metric)? We argue that business relationships derived from general literature (i.e., newspaper articles, news items etc.) may help us create a network of related companies (business networks). On the other hand, relative movement of stock prices can give us an indication of related companies (asset graphs). We also expect to see some relationships between these two kinds of networks. We adapt the asset graph construction approach from the literature for our asset graph implementations, and then, define our methodology for business network construction. Finally, an introduction to the exploration of some relationships between the asset graphs and business networks is presented.

Suggested Citation

  • Fatih Cavdur & Soundar Kumara, 2014. "Network mining: Applications to business data," Information Systems Frontiers, Springer, vol. 16(3), pages 473-490, July.
    • Handle: RePEc:spr:infosf:v:16:y:2014:i:3:d:10.1007_s10796-012-9355-z
    DOI: 10.1007/s10796-012-9355-z
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    References listed on IDEAS

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    Cited by:

    1. Gürdal Ertek & Gül Tokdemir & Mete Sevinç & Murat Mustafa Tunç, 2017. "New knowledge in strategic management through visually mining semantic networks," Information Systems Frontiers, Springer, vol. 19(1), pages 165-185, February.
    2. Xue Guo & Hu Zhang & Tianhai Tian, 2019. "Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data," Papers 1906.08088, arXiv.org.
    3. Yi-Shan Sung & Dashun Wang & Soundar Kumara, 2018. "Uncovering the effect of dominant attributes on community topology: A case of facebook networks," Information Systems Frontiers, Springer, vol. 20(5), pages 1041-1052, October.
    4. Guo, Xue & Li, Weibo & Zhang, Hu & Tian, Tianhai, 2022. "Multi-likelihood methods for developing relationship networks using stock market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).

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