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Dynamic network analysis of online interactive platform

  • Mehmet N. Aydin
  • N. Ziya Perdahci
Article

Abstract

The widespread use of online interactive platforms including social networking applications, community support applications draw the attention of academics and businesses. The basic trust of this research is that the very nature of these platforms can be best described as a network of entangled interactions. We agree with scholars that these platforms and features necessitate the call for theory of network as a novel approach to better understand their underpinnings. We examine one of the leading online interactive health networks in Europe. We demonstrate that the interactive platform examined exhibits essential structural properties that characterize most real networks. In particular, we focus on the largest connected component, so-called a giant component (GC), to better understand network formation. Dynamic network analysis helps us to observe how the GC has evolved over time and to identify a particular pattern towards emerging a GC. We suggest that the network measures examined for the platform should be considered as novel and complementary metrics to those used in conventional web and social analytics. We argue that various stages of GC development can be a promising indicator of the strength and endurance of the interactions on the platform. Platform managers may take into account basic stages of the emergence of the GC to determine varying degrees of product attractiveness.

Keywords

Online interactive platform Theory of network Social network analysis 

Notes

Acknowledgements

The authors were supported by Kadir Has Scientific Research Project Grant 2014-BAP-05.

The authors are grateful to the CEO of Doktorsitesi.com for his valuable comments and providing data. Complete mathematical specifications and data are available from the corresponding author on request.

References

  1. Anderson, C. R., & Zeithaml, C. P. (1984). Stage of the product life cycle, business strategy, and business performance. Academy of Management Journal, 27(1), 5–24.CrossRefGoogle Scholar
  2. Aydin, M. N., & Perdahci, N. Z. (2016). Network analysis of an interactive health network. Journal of Internet Social Networking & Virtual Communities, 2016(2016), 1.CrossRefGoogle Scholar
  3. Barabási, A.-L. (2009). Scale-free networks: a decade and beyond. Science, 325(5939), 412.CrossRefGoogle Scholar
  4. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.CrossRefGoogle Scholar
  5. Barabâsi, A.-L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3), 590–614.CrossRefGoogle Scholar
  6. Barrat, A., Barthelemy, M., & Vespignani, A. (2008). Dynamical processes on complex networks (Vol. 1). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  7. Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. ICWSM, 8, 361–362.Google Scholar
  8. Bosslet, G. T., Torke, A. M., Hickman, S. E., Terry, C. L., & Helft, P. R. (2011). The patient–doctor relationship and online social networks: results of a national survey. Journal of General Internal Medicine, 26(10), 1168–1174.CrossRefGoogle Scholar
  9. Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., et al. (2000). Graph structure in the web. Computer Networks, 33(1), 309–320.CrossRefGoogle Scholar
  10. Butler, B. S. (2001). Membership size, communication activity, and sustainability: a resource-based model of online social structures. Information Systems Research, 12(4), 346–362.CrossRefGoogle Scholar
  11. Chambers, D., Wilson, P., Thompson, C., & Harden, M. (2012). Social network analysis in healthcare settings: a systematic scoping review. PloS One, 7(8), e41911.CrossRefGoogle Scholar
  12. Chau, M., & Xu, J. (2012). Business intelligence in blogs: understanding consumer interactions and communities. MIS Quarterly: Management Information Systems, 36(4), 1189–1216.Google Scholar
  13. Chen, X., & Yang, C.-Z. (2010). Visualization of social networks. In B. Furht (Ed.), Handbook of social network technologies and applications (pp. 585–610): Springer, Boston.Google Scholar
  14. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), 1165–1188.Google Scholar
  15. Colizza, V., Barrat, A., Barthélemy, M., & Vespignani, A. (2006). The role of the airline transportation network in the prediction and predictability of global epidemics. Proceedings of the National Academy of Sciences of the United States of America, 103(7), 2015–2020.CrossRefGoogle Scholar
  16. Enders, A., Hungenberg, H., Denker, H.-P., & Mauch, S. (2008). The long tail of social networking.: revenue models of social networking sites. European Management Journal, 26(3), 199–211.CrossRefGoogle Scholar
  17. Fetterman, D. (2009). Data grows up: The architecture of the Facebook platform (pp. 89–109). Inc: O’Reilly Media.Google Scholar
  18. Ghose, A., & Yang, S. (2009). An empirical analysis of search engine advertising: sponsored search in electronic markets. Management Science, 55(10), 1605–1622.CrossRefGoogle Scholar
  19. Gneiser, M., Heidemann, J., Klier, M., Landherr, A., & Probst, F. (2012). Valuation of online social networks taking into account users’ interconnectedness. Information Systems and e-Business Management, 10(1), 61–84.CrossRefGoogle Scholar
  20. Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 1360–1380.Google Scholar
  21. Heidemann, J., Klier, M., & Probst, F. (2012). Online social networks: a survey of a global phenomenon. Computer Networks, 56(18), 3866–3878.CrossRefGoogle Scholar
  22. Johnson, S. L., Faraj, S., & Kudaravalli, S. (2014). Emergence of Power Laws in Online Communities: The Role of Social Mechanisms and Preferential Attachment. MIS Quarterly, 38(3), 795–808.Google Scholar
  23. Jones, Q., Ravid, G., & Rafaeli, S. (2004). Information overload and the message dynamics of online interaction spaces: a theoretical model and empirical exploration. Information Systems Research, 15(2), 194–210.CrossRefGoogle Scholar
  24. Kane, G. C., Alavi, M., Labianca, G., & Borgatti, S. P. (2014). What’s different about social media networks? A framework and research agenda. MIS Quarterly, 38(1), 275–304.Google Scholar
  25. Kim, J., & Wilhelm, T. (2008). What is a complex graph? Physica A: Statistical Mechanics and its Applications, 387(11), 2637–2652.CrossRefGoogle Scholar
  26. Kimura, M., Saito, K., Nakano, R., & Motoda, H. (2009). Finding influential nodes in a social network from information diffusion data. In M. J. Young, J. Salerno, H. Liu (Eds.), Social Computing and Behavioral Modeling (pp. 1–8): Springer, Boston.Google Scholar
  27. Kleinberg, J. (2000). The small-world phenomenon: An algorithmic perspective. In Proceedings of the thirty-second annual ACM symposium on Theory of computing, (pp 163–170): ACM, New York.Google Scholar
  28. Kossinets, G., & Watts, D. J. (2006). Empirical analysis of an evolving social network. Science, 311(5757), 88–90.CrossRefGoogle Scholar
  29. Kumar, R., Novak, J., & Tomkins, A. (2010). P. S. Yu, J. Han, C. Faloutsos (Eds.), Structure and evolution of online social networks. In Link mining: models, algorithms, and applications (pp 337–357): Springer, New York.Google Scholar
  30. Leskovec, J., Kleinberg, J., & Faloutsos, C. (2007). Graph evolution: densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 2.CrossRefGoogle Scholar
  31. Li, L., Xu, L., Jeng, H. A., Naik, D., Allen, T., & Frontini, M. (2008). Creation of environmental health information system for public health service: a pilot study. Information Systems Frontiers, 10(5), 531–542.CrossRefGoogle Scholar
  32. Ma, J., Zeng, D., & Zhao, H. (2012). Modeling the growth of complex software function dependency networks. Information Systems Frontiers, 14(2), 301–315.CrossRefGoogle Scholar
  33. Marsden, P. V. (1990). Network data and measurement. Annual Review of Sociology. doi: 10.1146/annurev.so.16.080190.002251.
  34. Milgram, S. (1967). The small world problem. Psychology today, 2(1), 60–67.Google Scholar
  35. Molloy, M., & Reed, B. (1998). The size of the giant component of a random graph with a given degree sequence. Combinatorics, Probability and Computing, 7(03), 295–305.CrossRefGoogle Scholar
  36. Moss, J., & Elias, B. (2010). Information networks in intensive care: a network analysis of information exchange patterns. In AMIA annual symposium proceedings, (Vol. 2010, pp. 522–526): American medical informatics association.Google Scholar
  37. Newman, M. E., Watts, D. J., & Strogatz, S. H. (2002). Random graph models of social networks. Proceedings of the National Academy of Sciences, 99(suppl 1), 2566–2572.CrossRefGoogle Scholar
  38. Orbach, Y., & Fruchter, G. E. (2014). Predicting product life cycle patterns. Marketing Letters, 25(1), 37–52.CrossRefGoogle Scholar
  39. Sen, A., Dacin, P. A., & Pattichis, C. (2006). Current trends in web data analysis. Communications of the ACM, 49(11), 85–91.CrossRefGoogle Scholar
  40. Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), 433–441.CrossRefGoogle Scholar
  41. Simon, H. A. (1955). On a class of skew distribution functions. Oxford University Press on behalf of Biometrika Trust. doi: 10.2307/2333389.
  42. Steinfield, C., Ellison, N. B., & Lampe, C. (2008). Social capital, self-esteem, and use of online social network sites: a longitudinal analysis. Journal of Applied Developmental Psychology, 29(6), 434–445.CrossRefGoogle Scholar
  43. Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268–276.CrossRefGoogle Scholar
  44. van Mierlo, T. (2014). The 1% rule in four digital health social networks: an observational study. Journal of Medical Internet Research, 16(2).Google Scholar
  45. Wang, Y., Zeng, D., Zhu, B., Zheng, X., & Wang, F. (2012). Patterns of news dissemination through online news media: a case study in China. Information Systems Frontiers, 1–14.Google Scholar
  46. Wasserman, S. & Faust K. (1994). Social network analysis: Methods and applications (Vol. 8): Cambridge: Cambridge University Press.Google Scholar
  47. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393(6684), 440–442.CrossRefGoogle Scholar
  48. Wijnhoven, F., & Kraaijenbrink, J. (2008). Product-oriented design theory for digital information services: a literature review. Internet research, 18(1), 93–120.CrossRefGoogle Scholar
  49. Zeng, D., Chen, H., Lusch, R., & Li, S.-H. (2010). Social media analytics and intelligence. Intelligent Systems, IEEE, 25(6), 13–16.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Faculty of Engineering and Natural SciencesKadir Has University, Central CampusİstanbulTurkey
  2. 2.Department of InformaticsMimar Sinan Fine Arts University, Bomonti CampusİstanbulTurkey

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