Social Network Metrics Integration into Fuzzy Expert System and Bayesian Network for Better Data Science Solution Performance

Chapter

Abstract

Basic parameters for social network analysis comprise social network common metrics. There are numerous social network metrics. During the data analysis stage, the analyst combines different metrics to search for interesting patterns. This process can be exhaustive with regard to the numerous potential combinations and how we can combine different metrics. In addition, other, non-network measures can be observed together with social network metrics. This chapter illustrates the proposed methodology for fraud detection systems in the insurance industry, where the fuzzy expert system and the Bayesian network was the basis for an analytical platform, and social network metrics were used as part of the solution to improve performance. The solution developed shows the importance of integrated social network metrics as a contribution towards better accuracy in fraud detection. This chapter describes a case study with a description of the phases of the process, from data preparation, attribute selection, model development to predictive power evaluation. As a result, from the empirical result, it is evident that the use of social network metrics within Bayesian networks and fuzzy expert systems significantly increases the predictive power of the model.

Keywords

Bayesian networks Fuzzy expert system Social network analysis Social network metrics 

References

  1. 1.
    Abraham, A., Hassanien, A-E., Snášel, V.: Computational Social Network Analysis Trends, Tools and Research Advances. Springer, London (2010)CrossRefGoogle Scholar
  2. 2.
    Aharony, N., Pan, W., Cory, I., Khayal, I., Pentland, A.: Social fMRI: Investigating and Shaping Social Mechanisms in the Real World, Pervasive Mob. Comput. 7(6), 643–659 (2011)CrossRefGoogle Scholar
  3. 3.
    Akoglu, L., Vaz de Melo, P.O.S., Faloutsos, C.: Quantifying Reciprocity in Large Weighted Communication Networks, PAKDD 2. Lecture Notes in Computer Science, vol. 7302, pp. 85–96. Springer, Berlin, Heidelberg (2012)Google Scholar
  4. 4.
    Altshuler, Y., Pan, W., Pentland, A.: Trends Prediction Using Social Diffusion Models, Social Computing, Behavioral-Cultural Modeling and Prediction. Lecture Notes in Computer Science Series, pp. 97–104. Springer, Berlin, Heidelberg (2012)Google Scholar
  5. 5.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)MATHGoogle Scholar
  6. 6.
    Carrington, P.J., Scott, J., Wasserman, S. (eds.): Models and Methods in Social Network Analysis, pp. 248–249. Cambridge University Press, Cambridge (2005)Google Scholar
  7. 7.
    Coser, L.A.: Masters of Sociological Thought: Ideas in Historical and Social Context, 2nd edn. Harcourt, New York, NY (1977)Google Scholar
  8. 8.
    D’Agostini, G.D.: Bayesian Reasoning in Data Analysis: A Critical Introduction. World Scientific, New York (2003)CrossRefMATHGoogle Scholar
  9. 9.
    Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, Cambridge (2010)CrossRefMATHGoogle Scholar
  10. 10.
    Erdös, P., Rényi, A.: On random graphs. Publ. Math. (Debrecen) 6, 290–297 (1959)Google Scholar
  11. 11.
    Erdös, P., Rényi, A.: On the evolution of random graphs. In: Publication of the Mathematical Institute of the Hungarian Academy of Sciences, vol. 5 (1960)Google Scholar
  12. 12.
    Erdös, P., Rényi, A.: On the strength of connectedness of a random graph. Acta Math. Acad. Sci. Hung. 12, 18–29 (1961)MathSciNetMATHGoogle Scholar
  13. 13.
    Freeman, L.C.: The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press, Vancouver, BC (2004)Google Scholar
  14. 14.
    Fuller, R., Carlsson, C.: Fuzzy Reasoning in Decision Making and Optimization. Physica-Verlag, Heidelberg (2002)MATHGoogle Scholar
  15. 15.
    Hampel, R., Wagenknecht, M., Chaker, N. (eds.): Fuzzy Control: Theory and Practice. Physica, HeidelbergGoogle Scholar
  16. 16.
    Jackson, M.O.: Social and Economic Networks. Princeton University Press, Princeton, NJ (2010)MATHGoogle Scholar
  17. 17.
    Jaynes, E.T.: Probability Theory. The Logic of Science. Cambridge University Press, Cambridge (2003)CrossRefMATHGoogle Scholar
  18. 18.
    Jensen, F., Nielsen, T.: Bayesian Networks and Decision Graphs. Springer, New York (2007)CrossRefMATHGoogle Scholar
  19. 19.
    Kjarluff, U., Madsen, A.: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Springer, New York (2013)Google Scholar
  20. 20.
    Klepac, G.: Discovering behavioural patterns within customer population by using temporal data subsets. In: Bhattacharyya, S., Banerjee, P., Majumdar, D., Dutta, P. (eds.) Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications, pp. 321–348. IGI Global, Hershey, PA (2016)Google Scholar
  21. 21.
    Klepac, G., Kopal, R., Mršić, L.: Developing Churn Models Using Data Mining Techniques and Social Network Analysis, pp. 1–308. IGI Global, Hershey, PA (2015). doi:10.4018/978-1-4666-6288-9Google Scholar
  22. 22.
    Klepac, G., Kopal, R., Mršić, L.: REFII model as a base for data mining techniques hybridization with purpose of time series pattern recognition. In: Bhattacharyya, S., Dutta, P., Chakraborty, S. (eds.) Hybrid Soft Computing Approaches, pp. 237–270. Springer, New York (2015)Google Scholar
  23. 23.
    Lauritzen, S.L., Nilsson, D.: Representing and solving decision problems with limited information. Manage. Sci. 47, 1238–1251 (2001)CrossRefMATHGoogle Scholar
  24. 24.
    Leonides, C.: Fuzzy Logic and Expert Systems Applications. Academic, New York (1998)Google Scholar
  25. 25.
    Milgram, S.: The small-world problem. Psychol. Today 1(1), 61–67 (1967)Google Scholar
  26. 26.
    Moreno, J.L.: Sociometry, Experimental Method, and the Science of Society. Beacon House, Ambler, PA (1951)Google Scholar
  27. 27.
    Pinheiro, C.A.R.: Social Network Analysis in Telecommunications. Wiley, Hoboken, NJ (2011)Google Scholar
  28. 28.
    Remer, R.: Chaos theory links to Morenean theory: a synergistic relationship. J. Group Psychother. Psychodrama Sociom. 59, 38–45 (2006)Google Scholar
  29. 29.
    Scott, J.: Social Network Analysis: A Handbook. Sage Publications, London (1987)Google Scholar
  30. 30.
    Simmel, G.: How is society possible? In: Levine, D. (ed.) On Individuality and Social Forms. University of Chicago Press, Chicago, IL (1908/1971)Google Scholar
  31. 31.
    Zadeh, L.A., Kacprzyk, J. (eds.): Fuzzy Logic for the Management of Uncertainty. Wiley, New York (1992)Google Scholar
  32. 32.
    Zhang, M.: Social network analysis: history, concepts, and research. In: Fuhrt, B. (ed.) Handbook of Social Network Technologies and Applications, pp. 3–22. Springer, New York, NY (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Raiffeisen Bank AustriaZagrebCroatia
  2. 2.IN2DataZagrebCroatia

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