Associating Human-Centered Concepts with Social Networks Using Fuzzy Sets



The rapidly growing global interconnectivity, brought about to a large extent by the Internet, has dramatically increased the importance and diversity of social networks. Modern social networks cut across a spectrum from benign recreational focused websites such as Facebook to occupationally oriented websites such as LinkedIn to criminally focused groups such as drug cartels to devastation and terror focused groups such as Al-Qaeda. Many organizations are interested in analyzing and extracting information related to these social networks. Among these are governmental police and security agencies as well marketing and sales organizations. To aid these organizations there is a need for technologies to model social networks and intelligently extract information from these models. While established technologies exist for the modeling of relational networks [1–7] few technologies exist to extract information from these, compatible with human perception and understanding. Data bases is an example of a technology in which we have tools for representing our information as well as tools for querying and extracting the information contained. Our goal is in some sense analogous. We want to use the relational network model to represent information, in this case about relationships and interconnections, and then be able to query the social network using intelligent human-centered concepts. To extend our capabilities to interact with social relational networks we need to associate with these network human concepts and ideas. Since human beings predominantly use linguistic terms in which to reason and understand we need to build bridges between human conceptualization and the formal mathematical representation of the social network. Consider for example a concept such as “leader”. An analyst may be able to express, in linguistic terms, using a network relevant vocabulary, properties of a leader. Our task is to translate this linguistic description into a mathematical formalism that allows us to determine how true it is that a particular node is a leader. In this work we look at the use of fuzzy set methodologies [8–10] to provide a bridge between the human analyst and the formal model of the network.


Social Network Linguistic Term Fuzzy Subset Authority Figure Membership Grade 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Carrington, P. J., Scott, J. and Wasserman, S., Models and Methods in Social Network Analysis, Cambridge University Press: New York, 2007.Google Scholar
  2. 2.
    Wasserman, S. and Faust, K., Social Network Analysis: Methods and Applications, Cambridge University Press: New York, 1994.Google Scholar
  3. 3.
    Scott, J., Social Network Analysis, SAGE: Los Angeles, 2000.Google Scholar
  4. 4.
    Chartrand, G., Introductory Graph Theory, Dover Publications: Mineola, NY, 1977.Google Scholar
  5. 5.
    Bollobas, B., Modern Graph Theory, Springer: New York, 2000.Google Scholar
  6. 6.
    Berge, C., The Theory of Graphs, Dover Publications: Mineola, NY, 2001.Google Scholar
  7. 7.
    Newman, M., Barabási, A. L. and Watts, D. J., The Structure and Dynamics of Networks, Princeton University Press: Princeton, NJ, 2006.Google Scholar
  8. 8.
    Zadeh, L. A., “Fuzzy sets,” Information and Control 8, 338–353, 1965.MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Yager, R. R. and Filev, D. P., Essentials of Fuzzy Modeling and Control, Wiley: New York, 1994.Google Scholar
  10. 10.
    Nguyen, H. T. and Walker, E. A., A First Course in Fuzzy Logic, Chapman and Hall: Boca Raton, FL, 2005.Google Scholar
  11. 11.
    Zadeh, L. A., “Fuzzy logic = computing with words,” IEEE Transactions on Fuzzy Systems 4, 103–111, 1996.CrossRefGoogle Scholar
  12. 12.
    Zadeh, L. A., “Generalized theory of uncertainty (GTU) – principal concepts and ideas,” Computational Statistics and Data Analysis 51, 15–46, 2006.MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Bargiela, A. and Pedrycz, W., Granular Computing: An Introduction, Kluwer Academic: Amsterdam, 2003.MATHGoogle Scholar
  14. 14.
    Zadeh, L. A., “A computational approach to fuzzy quantifiers in natural languages,” Computing and Mathematics with Applications 9, 149–184, 1983.MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Yager, R. R., “On ordered weighted averaging aggregation operators in multi-criteria decision making,” IEEE Transactions on Systems, Man and Cybernetics 18, 183–190, 1988.MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Pedrycz, W. and Gomide, F., Fuzzy Systems Engineering: Toward Human-Centric Computing, Wiley: New York, 2007.Google Scholar
  17. 17.
    Yager, R. R., “Quantifier guided aggregation using OWA operators,” International Journal of Intelligent Systems 11, 49–73, 1996.CrossRefGoogle Scholar
  18. 18.
    Yager, R. R., “Applications and extensions of OWA aggregations,” International Journal of Man-Machine Studies 37, 103–132, 1992.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Machine Intelligence InstituteIona CollegeNew RochelleUSA

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