Information Systems Frontiers

, Volume 16, Issue 1, pp 153–162 | Cite as

A network view of business systems



In this paper, we present a novel business network construction approach, where the nodes of the network correspond to the names of the companies in a particular stock market index, and its links show the co-occurrence of two company names in daily news. Our approach consists of two phases, in which search for the company names in the news articles and network construction operations are performed, respectively. To increase the quality of results, each article is classified as business news or not business news before these operations, and only the articles that are classified as business news are considered for network construction. The resulting network presents a visualization of the business events and company relationships during the corresponding time period. We study both co-occurrences as well as single occurrences of company names in the articles scanned in our analysis.


Algorithms Business systems Financial systems Networks Text mining 

Supplementary material

10796_2012_9354_MOESM1_ESM.xls (63 kb)
ESM 1(XLS 63 kb)


  1. Albert, R., Jeong, H., & Barabasi, A.-L. (1999). Diameter of the world wide web. Nature, 401, 130–131.CrossRefGoogle Scholar
  2. Barabasi, A. L. (2003). Scale-free networks. Scientific American, 288, 50–59.CrossRefGoogle Scholar
  3. Barabasi, A. L. (2007). Network medicine—from obesity to the “diseasome”. The New England Journal of Medicine, 357(4), 404–407.CrossRefGoogle Scholar
  4. Barabasi, A. L. (2009). Scale-free networks: a decade and beyond. Science, 325, 412–413.CrossRefGoogle Scholar
  5. Batagelj, V., & Mrvar, A. (2009). Pajek—program for large network analysis, Accessed 20 July 2010.
  6. Bengtsson, M., & Kock, S. (1999). Cooperation and competition in relationships between competitors in business networks. Journal of Business and Industrial Marketing, 14(3), 178–194.CrossRefGoogle Scholar
  7. Costa, L. D., Oliveira, O. N. Jr., Travieso, G., Rodrigues, F. A., Boas, P. R., & Antiqueira, L. (2008). Analyzing and modeling real-world phenomena with complex networks: a survey of applications, arXiv:0711.3199v3 [physics.soc-ph].Google Scholar
  8. Dezso, Z., Almaas, E., Lukacs, A., Racz, B., Szakadat, I., & Barabasi, A.-L. (2006). Dynamics of information access on the web. Physical Review E, 73, 066132.CrossRefGoogle Scholar
  9. Dunne, J. A., Williams, R. J., & Martinez, N. D. (2002). Food-web structure and network theory: the role of connectance and size. Proceedings of the National Academy of Sciences, 99, 12,917–12,922.CrossRefGoogle Scholar
  10. Erdos, P., & Renyi, A. (1959). On random graphs I. Publicationes Mathematicae Debrecen, 6, 290–297.Google Scholar
  11. Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabasi, A.-L. (2007). Human disease network. PNAS, 104(21), 8,685–8,690.CrossRefGoogle Scholar
  12. Guimera, R., Mossa, S., Turtschi, A., & Amaral, L. A. N. (2005). The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. PNAS, 102(22), 7,794–7,799.CrossRefGoogle Scholar
  13. Krebs, V. (2006). Social network analysis, Accessed 10 May 2010.
  14. Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Pysical Journal B, 11, 193–197.CrossRefGoogle Scholar
  15. Onnela, J. P. (2006). Complex networks in the study of financial and social systems. Ph.D. Thesis, Laboratory of Computational Engineering, Helsinki University of Technology, Finland.Google Scholar
  16. Onnela, J. P. (2002). Taxonomy of financial assets. M.S. Thesis, Helsinki University of Technology, Finland. Google Scholar
  17. Onnela, J. P., Chakraborti, A., Kaski, K., & Kertesz, J. (2003a). Dynamic asset trees and Black Monday. Physica A, doi:10.1016/S0378-4371(02)01882-4.
  18. Onnela, J.-P., Chakraborti, A., Kaski, K., Kertesz, J., & Kanto, A. (2003b). Asset trees and asset graphs in financial markets. PhysicaScripta, T106, 48–54.Google Scholar
  19. Onnela, J. P., Kaski, K., & Kertesz, J. (2004). Clustering and information in correlation based financial networks. The European Pyhsical Journal B, 38, 353–362.CrossRefGoogle Scholar
  20. Onnela, J. P., Saramaki, J., Kaski, K., & Kertesz, J. (2006). Financial market—a network perspective. practical fruits of econophysics, Nikkei econophysics III proceedings, Springer, Tokyo, 302–206.Google Scholar
  21. Steyvers, M., & Tenenbaum, J. B. (2005). The large-scale structure of semantic networks: statistical analyses and a model of semantic growth. Cognitive Science, 29(1), 41–78.CrossRefGoogle Scholar
  22. Stouffer, D. B. (2010). Scaling from individuals to networks in food webs. Functional Ecology, 24, 44–51.CrossRefGoogle Scholar
  23. Williams, R. J., Berlow, E. L., Dunne, J. A., Barabasi, A. L., & Martinez, N. D. (2002). Two degrees of separation in complex food webs. Proceedings of the National Academy of Sciences, 99, 12913–12916.CrossRefGoogle Scholar
  24. Yıldırım, M. A., Goh, K.-I., Cusick, M. E., Barabási, A.-L., & Vidal, M. (2007). Drug-target network. Nature Biotechnology, 25(10), 1,119–1,126.CrossRefGoogle Scholar
  25. Yook, S. H., Jeong, H., & Barabasi, A.-L. (2002). Modeling the internet’s large-scale topology. Proceedings of the National Academy of Sciences, 99, 13,382–13,386.CrossRefGoogle Scholar
  26. Zaversnik, M., & Batagelj, V. (2000). Generalized cores, arXiv:cs/0202039v1.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Industrial EngineeringUludag UniversityBursaTurkey
  2. 2.Department of Industrial and Manufacturing EngineeringPennsylvania State UniversityUniversity ParkUSA

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